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Objectives: The dynamic and high spatial resolution data obtained by optical video satellites provide a new technical means for the dynamic observation of the earth. As a new generation of intelligent mapping remote sensing scientific test satellite, Luojia3-01 satellite was launched in early 2023, and it could acquire high-definition color video by gazing mode. However, the original video frame rate is low, only 6 frame per-second. To further improve the fluency and reduce the stuttering effect of visual perception, this paper carries out related research on intelligent video frame interpolation (VFI) of Luojia3-01 satellite video. Methods: First, a satellite video image stabilization method based on the perspective transformation model is proposed after analyzing the imaging errors during the gaze mode. Second, satellite video is greatly different from daily video, and there is no available satellite VFI dataset at present. This paper builds a VFI dataset termed as Luojia3_VFISet based on the stabilized satellite videos, which covers different scenes. Finally, based on the flow-agnostic video representation (FLAVR) VFI network without optical flow module, the FLAVR_Plus VFI network is proposed to further improve the interpolation effect for satellite video by in⁃ troducing the feature coding information of different scales from the encoder into the decoding process. Results: The experimental results show that the interpolation of FLAVR_Plus VFI network raises the peak signal to noise ratio (PSNR) and structural similarity (SSIM) to 35.544 6 dB and 0.917 9, respectively, and it enhances the quality of synthesized intermediate frames with hardly-observed artifacts under different scenarios. Compared with other methods, the proposed network can improve the PSNR by 0.5% to 7.2% and the SSIM by 0.5% to 8.7%, respectively. Conclusions: In this paper, the application of satellite VFI is studied by taking Luojia3-01 satellite as an example. The proposed Luojia3_VFISet dataset is conducive to the development of related research. The proposed FLAVR_Plus VFI network can effectively improve the fluency of the Loujia3-01 satellite video by generating interframes without artifacts and provide more interframe information for subsequent applications. [ABSTRACT FROM AUTHOR]

Objectives: Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning. Methods: The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset. Results: The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively. Conclusions: The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters. [ABSTRACT FROM AUTHOR]

Objectives: Vegetation plays an important role in ecological environment monitoring. Studying vegetation cover change can provide reference for regional ecological environment protection. The region of Altay in Xinjiang belongs to the ecological function zone of water conservation mountain grassland, with rich natural resources and beautiful scenery. However， the ecological environmental problems were gradually emerging under the national development strategies. Therefore, monitoring the dynamics of vegetation change in the area with multi-source remote sensing data is necessary to explore the relationship between the economic development and ecological environment protection. Methods: This paper uses multisource remote sensing long time series data with breaks for additive seasonal and trend, topographical position and geostatistical analysis methods to monitor the vegetation cover dynamics in Altay of Xinjiang, China during 2000—2019. Results: This paper processed large number of multisource remote sensing data along with human activity data and showed that during the study period, the number of vegetation breakpoints increased year by year from 2003 to 2009, and then gradually decreased after 2009. Meanwhile, seven types of vegetation breakpoint trends were detected, among them, the more number of breakpoints from degradation to growth type was identified, and the types of disturbance degradation and continuous degradation were less. From the interannual change curve, the dynamic change of vegetation coverage showed a trend of first decreasing and then increasing, that is, the vegetation coverage showed a degradation trend from 2000 to 2008 and a significant improvement trend from 2008 to 2019, and the degradation was greater than the improvement. Meanwhile, the east slope with an elevation of more than 900 m and a slope of more than 15° (from north to east and from south to east) is the dominant topographic location of vegetation degradation type, accounting for 62.4%. Conclusions: The protection of ecological environment in Altay of Xinjiang still needs to be further strengthened and effective protection measures should be taken. [ABSTRACT FROM AUTHOR]

Objectives: It is difficult for the traditional traffic information acquisition method to obtain the real-time dynamic traffic flow information of large scope and full coverage. With urbanization, the increasing number of supertall buildings makes the city skyline a favorable platform for earth observation. This paper studies the urban remote sensing video production and traffic information extraction method using the urban skyline as the observation platform. Methods: First, the earth observation data shooting is carried out in the super-tall buildings. Then, the original oblique observation data is corrected by orthography, which is further fused with the satellite image to generate a large range of remote sensing video data of the city. Finally, we train a deep learning model and use it for vehicle classification and identification. The number and density of vehicles in the area are calculated based on the identification results. Results: This paper carries out data collection and processing analysis on the sightseeing floor of Ping, an Building in Shenzhen. Results show that the proposed method can produce low-cost, long-time, large-scale and high-quality urban remote sensing video. The vehicle detection based on the remote sensing video have high accuracy. Conclusions: The proposed framework can be used to effectively monitor the regional traffic flow and sever for the smart city management. Furthermore, based on its capability of long-time, high-resolution, full-coverage and real-time remote sensing, this method can also be applied to many other urban management fields, such as urban disaster emergency response, crowd monitoring, and construction site monitoring. [ABSTRACT FROM AUTHOR]

The Yangtze River Basin is an important part of China's ecology and social economy, and the land use/cover status is experiencing rapid changes. As the development of remote sensing technology, the Internet of things and social media, we can obtain huge, multi-dimensional relevant data of land use/cover, the process of land use/cover, driving factors and ecological response of research, the development of the Yangtze river basin planning and ecological protection provides a decision-making basis. First, this paper reviews the current research on land use/cover change in the Yangtze river basin, and then expounds the re⁃ search methods and conclusions in terms of the driving mechanism and ecological effects of land use/cover change. Finally, this paper discusses the difficulties and challenges faced by existing researches from the differences of land use/cover products, regional driving factors, complexity of ecological environmental change process and comprehensive evaluation of ecological service value and the possible directions of future researches. [ABSTRACT FROM AUTHOR]

Objective: Deep convolutional neural network (DCNN) is widely used in automatic road extraction from high-resolution remote sensing images (HRSIs). However, the existing methods make it difficult to model the context relationship of pixels in the predicted results. To solve this problem, some studies have used fully connected conditional random field (FullCRF) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. Methods: In order to improve the integrity of road structure, this paper proposes a short range conditional random filed (SRCRF) model combined with DCNN. SRCRF mainly includes a unary potential function based on road pre-segmentation, a binary potential function based on spectral spatial features, and a K-neighborhood mean field inference algorithm. First, the prior knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF. Then, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency,that is, adjacent pixels with similar spectral features have the same label. Finally, the K-neighborhood mean field inference algorithm based on the mean-field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors. Results: The experimental results show that SRCRF not only alleviates the transition smoothness of FullCRF, but also alleviates the structural discontinuity and incompleteness in the road acquisition results of HRSIs. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1-scores of SRCRF are increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCRF. Conclusions: The proposed SRCRF combines the advantages of DCNN, optimizes the fully connected structure of traditional conditional random fields into the K-neighborhood structure, reduces the inference scope, and improves the proportion of feature vectors. Compared with FullCRF, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. The performance of SRCRF is improved compared with DCNN and FullCRF, and the time is shortened by one order of magnitude compared with FullCRF. In the future, we will further investigate the potential for learning Gaussian features and investigate more complex architecture of conditional random field to better capture global context information. Additionally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc. [ABSTRACT FROM AUTHOR]

Low spatial resolution is one of the main bottlenecks restricting the fine application of remote sensing parameters. Spatial downscaling is an effective way to improve their spatial resolution and application capabilities. Researchers have developed various downscaling methods for different remote sensing parameters. However, a unified and general method classification system has not yet been formed. Based on the in-depth commonality analysis of the downscaling of various parameters, this paper systematically summarizes the spatial downscaling methods from the perspective of data fusion, taking into account the required complementary information. Four types of downscaling methods are summarized: Multi-parameter fusion, spatiotemporal fusion, remote sensing-model fusion, and super-resolution reconstruction implicit fusion. The advantages, disadvantages, and applicable scenarios of various methods are analyzed, and the development trend of spatial downscaling method research is discussed. It can provide theoretical and technical support for improving the refined application ability of remote sensing data. [ABSTRACT FROM AUTHOR]

The nonlinear remote sensing ecological index (nRSEI) is a recently proposed eco⁃ logical index, which used the kernel principal component analysis (kPCA) algorithm to integrate four indica⁃ tors of the existing remote sensing ecological index (RSEI) rather than using the traditional principal compo⁃ nent analysis (PCA) technique. The main reason for using kPCA was that in the Beijing area the correla⁃ tions between wetness, greenness, dryness, and heat that are four indicators used in RSEI were generally weak, so the kPCA that is specially developed to deal with variables with nonlinear relationship was needed to handle these four non-linear indicators. This paper aims to examine the correlation strength of these four indicators in the Beijing area to see whether their relationship is strong or weak and analyzes the effective⁃ ness of the accuracy assessment method that was used for the validation of the new index. Methods: Through examining correlation coefficients and scatter diagrams, the correlation between the four indicators is investigated to find out whether the relationship between the indicators is linear or non-linear. Also, the effectiveness of the validation for the new index is analyzed. Results: The results show that the four indica⁃ tors are strongly linearly correlated with each other, therefore, the kPCA was not suitable for the intergra⁃ dation of the four indicators. The methods used for the accuracy assessment of the new index also have obvious defects and thus failed to effectively validate the accuracy of the new index. In addition, some important issues related to prepare remote sensing papers are also discussed. These include the feasibility of remote sensing modeling, the robustness of the model, the scale consistency of the sub-indicators in the model, the validation method for the model, the selection of reference images for the validation, and the required sample size for accuracy validation. Conclusions: The nRSEI is not a suitable index for the assessment of regional ecological status as it mistakenly employed kPCA to intergrade the four indicators that are linearly related. [ABSTRACT FROM AUTHOR]

Objectives: It is of great importance to precisely acquire the spatial distribution and dynamic variations of plastic greenhouses for agricultural development planning, grain assessment, and ecological environment monitoring. High-resolution remote sensing images offer detailed information of plastic greenhouse shapes and boundaries, satisfies the requirements of precision agriculture, and have irreplaceable advantages in conducting precise surveys of plastic greenhouses in crucial regions. Nevertheless, the mapping model of plastic greenhouses meets the challenges including inadequate prior information and unbalance between mapping accuracy and model complexity. Methods: This paper introduces a semantic segmentation model for plastic greenhouse that integrates the advanced plastic greenhouse index (APGI) and high-resolution red green blue (RGB) images. The proposed model primarily comprises the APGI semantic branch, RGB semantic branch, and RGB detail branch, and it incorporates an attention mechanism to effectively merge reliable prior information from the APGI index and spatial detail information derived from high-resolution RGB images. Furthermore, a model training mechanism guided by boundary information is proposed to enhance the precise recognition capability of the proposed model for plastic greenhouse boundary pixels. Results: The study reveals that, within an attention-guided fusion framework, the medium-resolution APGI can significantly enhance the identification capabilities of plastic greenhouses in high-resolution images. The proposed model can not only improve the mapping accuracy of plastic greenhouses but also greatly simpl-ifie the parameter size and computational complexity of the semantic segmentation model. Conclusions: This study meets the demands for rapid and accurate extraction of plastic greenhouse in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Objectives: Traditional remote sensing-based air temperature estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Methods: Taking the Yangtze River Economic Zone as a typical research area, this paper introduces the geographically and temporally neural networks for high-precision temperature estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused to obtain the spatially continuous near-surface temperature. The model performance was evaluated by the site-based ten-fold cross-validation method. Results: The results show that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the root mean square error(RMSE) =1.899 ℃, mean absolute error (MAE) =1.310 ℃ and correlation coefficient (R) =0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112 ℃ and 0.378 ℃, respectively. Conclusions: The temperature mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for temperature estimation with high precision. [ABSTRACT FROM AUTHOR]

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Objectives: Satellite mission planning is an important way to rationally allocate satellite resources and obtain effective data. In order to solve the problem of resource waste in the absence of user demand for Qimingxing-1(QMX-1), we proposed a semantic description driven autonomous mission planning method for QMX-1. Methods: By analyzing the characteristics of earth observation targets and their requirements for remote sensing data, we have construct a semantic description model for global tasks and form a global earth observation target database. Based on the description model, we design a satellite autonomous mission planning method. By building a mission planning model, we can achieve autonomous planning for high-value targets and maximize the value of satellite in orbit applications. Results and Conclusions: We verifyed the proposed model based on the QMX-1 and proved the feasibility of the method in this paper. The model is applicable to different types of remote sensing satellites and can play a certain value in the future remote sensing constellation real-time intelligent service applications. [ABSTRACT FROM AUTHOR]

Objectives: The low-orbit giant constellation is an important new infrastructure of the country, and it is also the frontier and hot spot of the current development of the aerospace field. Methods: In this paper, through the “cloud-edge-end” dynamic optimization collaboration of satellite clusters and ground resources, the key issues of cloud-edge-end collaborative intelligent service for low-orbit giant constellation are studied. Focusing on the goal of one satellite multi-purpose, star cluster networking, collaborative sensing, autonomous control, and intelligent service, we analyze the key technologies such as intelligent control and high-precision processing of ground center cloud, collaborative observation and fusion processing of space-based edge cloud, real-time processing and intelligent service of satellite and user. Results and Conclusions: Based on the characteristics of observation targets, we have formed a“cloud-edge-end”collaborative intelligent service model for real-time tasks, which can provide fast, accurate and flexible space-time continuous information service for typical real-time application scenarios, and realize the leapfrog development of intelligent earth observation. [ABSTRACT FROM AUTHOR]

Objectives: In the remote sensing (RS) big data era, intelligent interpretation of remote sensing images (RSI) is the key technology to mine the value of big RS data and promote several important applications. Traditional knowledge-driven RS interpretation methods, represented by expert systems, are highly interpretable, but generally show poor performance due to the interpretation knowledge being difficult to be completely and accurately expressed. With the development of deep learning in computer vision and other fields, it has gradually become the mainstream technology of RSI interpretation. However, the deep learning technique still has some fatal flaws in the RS field, such as poor interpretability and weak generalization ability. In order to overcome these problems, how to effectively combine knowledge inference and data learning has become an important research trend in the field of RS big data intelligent processing. Generally, knowledge inference relies on a strong domain knowledge base, but the research on RS knowledge graph (RS-KG) is very scarce and there is no available large-scale KG database for RSI interpretation now. Methods: To overcome the above considerations, this paper focuses on the construction and evolution of the RS-KG for RSI interpretation and establishes the RS-KG takes into account the RS imaging mechanism and geographic knowledge. Supported by KG in the RS field, this paper takes three typical RSI interpretation tasks, namely, zero-shot RSI scene classification, interpretable RSI semantic segmentation, and large-scale RSI scene graph generation, as examples, to discuss the performance of the novel generation RSI interpretation paradigm which couples KG and deep learning. Results and Conclusions: A large number of experimental results show that the combination of RS-KG inference and deep da‐ ta learning can effectively improve the performance of RSI interpretation. The introduction of RS-KG can effectively improve the interpretation accuracy, generalization ability, anti-interference ability, and interpretability of deep learning models. These advantages make RS-KG promising in the novel generation RSI interpretation paradigm. [ABSTRACT FROM AUTHOR]

Objectives: As an emerging airborne remote sensing system, unmanned aerial vehicle (UAV) falls into multiple categories with various payloads, such as the multi ⁃ rotor and fixed ⁃ wing ones. Despite the harsh climatic condition, UAV is widely used in many Antarctic fields of basic and applied science, which still lacks a comprehensive and systematic literature review. Methods: We firstly discuss the special impact of Antarctic environmental conditions (meteorology, electromagnetic field, light, etc.) on the UAV operation. A comprehensive literature retrieval is subsequently presented on the current application of UAV in Antarctic research and expedition. We sort out 104 papers according to the time of publication, main journals, study areas, nations, and institutions. Representative literature is reviewed in seven application areas, including aeromechanics, atmosphere, sea ice and iceberg, glacier, geomorphology and geomagnetism, ecology-vegetation as well as ecology-animals. We retrospect the development and achievement of UAVs applications in the Chinese national Antarctic research and summarize the limitations of the research on the application of UAV in Antarctica. Results: A number of environmental factors need to be considered before the UAV missions, such as the meteorological conditions, electromagnetic field, solar radiations, and flight regulations. According to the review, half of the literature belongs to the journal paper, mostly in Polar Biology, Polar Science, and Remote Sensing. The earliest UAV research in Antarctica was published in 2004, followed by a productive period of International Polar Year in 2008. The first-tier countries including the USA, Australia, and Germany, led the progress in the research on UAVs in Antarctica. Meanwhile, the dominant role of top universities stood out via various collaborations. The UAV can also be classified into multiple categories according to the payload, such as the industrial⁃ or consumer-grade optical cameras, radiosonde, synthetic aperture radar, and light detecting and ranging （LiDAR), among which consumer-grade camera is widely used in Antarctic investigations. China􀆳s Antarctic expedition team initiated the Antarctic UAV program in 2007 and had carried out at least 18 flight missions by 2020. The flights covered Zhongshan Station, The Great Wall Station, Inexpression Island, and the inland ice sheet, from which the collected data were employed to support the glaciological, geomorphic, and biological studies. Conclusions: The UAV remote sensing, as the essential technology in the “Air ⁃Space ⁃Ground” polar observation system, has been increasingly upgraded in the recent decade. The flight experiments covered the primary research topics and research fields in Antarctic science. In general, the application and development of Antarctic UAV in China lie in the second tier, falling behind the USA and Australia. In the end, according to the current development of Antarctic UAV in China, this paper provides guidance for China's Antarctic expedition team in the future: (1) Develop new UAV models; (2) Make breakthroughs in the battery technology; (3) Couple multiple sensors; (4) Encourage trans ⁃disciplinary collaboration; (5) Pro-mote foreign communication and sharing; (6) Participate in the international management. [ABSTRACT FROM AUTHOR]

Objectives: The registration of remote sensing image data and GIS ( geographic information system ) vector data is the basis of the integration of remote sensing and GIS, which is widely used in the fields of map data update, city monitoring, map change detection and so on. At present, the key to the registration of remote sensing images and vector data is the extraction of remote sensing image features. However, the existing remote sensing image feature extraction has problems such as incomplete feature extraction, which leads to registration failure or low accuracy. This paper proposes a registration method for remote sensing images and vector data based on Mask region -based convolutional neural network (Mask R -CNN). Methods: Firstly, we select the road intersection as the distinctive feature of the same name in the remote sensing image and vector data, and create a road intersection image data set to train Mask R -CNN model. Secondly, according to the geometric topological relationship, the vector data road intersections are selected as vector control points. And take the intersection control point of the vector data as the center, we use 400 × 400 pixels window to crop the remote sensing image data and input it into the Mask R-CNN model, extract the border of the road intersection in the image. The control points of the same name are determined according to the Euclidean distance between the remote sensing image and the vector data control points, and the control points of the same name are cleaned using the density-based spatial clustering of applications with noise algorithm. Finally, the affine transformation parameters are calculated according to the filtered control points of the same name to realize the registration of remote sensing image and vector data. The registration data of Shanghai vector data and Gaofen-2 image data were selected for registration experiment. Results: Experimental results show that the average deviations of the experimental data before were 15. 34 m and 1. 44 m, before and after registration based on Mask R -CNN. This method can correctly register remote sensing images and vector data, and the proposed method has better application prospects in urban data registration, and has the characteristics of strong robustness and high accuracy. Conclusions: The proposed method in this paper can automatically register remote sensing images and vector data from different sources in areas such as mountains, grasslands, and deserts. In the next step, the amount of sample data of remote sensing images will be increased, including types of remote sensing images and types of features. [ABSTRACT FROM AUTHOR]

Objectives: In order to solve the problems of the singleness of activity metric construction in fusion rules and the subjectivity of parameter setting of pulse-coupled neural network (PCNN), a remote sensing image fusion method combining low-level visual features and parameter adaptive pulse coupled neural network (PAPCNN) in the non-subsampled shearlet transform (NSST) domain is proposed in this paper. Methods: First, the panchromatic image and the luminance component Y in YUV color space of multi⁃ spectral image are decomposed by NSST to obtain the high and low frequency components. Second, a fusion rule based on low-level visual features is used to low-frequency components fusion, and a new activity measure is constructed by combining three low-level features, namely, local phase congruency, local abrupt measure and local energy information. Then, PAPCNN model is used to high-frequency components fusion, and the multi-scale morphological gradient is used as the external input signal of the model. Finally, the fused image is obtained through NSST inverse transform and YUV inverse transform in turn. Results: The experimental results show that this method has better performance in remote sensing images of different platforms and different ground features. Compared with other 11 methods, this method has ab⁃ solute advantages in all evaluation indexes. Conclusions: The proposed method can better preserve the spatial and spectral information in the original image, thus it can provide a fused image with complementary ad⁃ vantages. [ABSTRACT FROM AUTHOR]

Objectives：With the acceleration of urbanization and the rapid expansion of urban population, the population data on the street scale plays an important role in urban economic, social, resource and environmental development. Therefore, estimating population on urban street scales with high‐resolution remote sensing images is of great theoretical and practical value for promoting sustainable urban development. Methods：This paper uses the building information in remote sensing images and census data to analyze the correlation between street building information and population data, and the city street scale population estimation models based on building information are proposed. The best estimation model for the number and geometric characteristics of buildings and population is established based on the multivariate stepwise regression method and the Akaike information criterion that determine the significant feature variables of the building.Results：The Experiments show that the population estimation model proposed in this paper can estimate the population of street scale with high accuracy. On the one hand, the floor data can be directly applied to the estimation model. On the other hand, the floor data is also the basis for classifying street buildings. Dividing street buildings into low‐rise, middle‐rise and high‐rise buildings according to the number of floors can significantly improve the population estimation effect of the model. Conclusions：The population estimation model proposed in this paper can estimate the population of street scale with high accuracy. This paper uses street‐scale data in administrative divisions, but the estimation range of the proposed model is theoretically also valid in population estimation across street areas, which needs to be verified in subsequent studies. [ABSTRACT FROM AUTHOR]

Style transfer which originates from computer graphics has attracted broader attention in the field of cartography， considerable efforts have been made on cartographic style transfer algorithms and experimental evaluation. However, it also suffers from unclear demarcation of map style, and lack of evaluation of style transfer results. Therefore, firstly, this paper conceptually analyzes the idea of map style and the applicable scenarios of styled maps. Then, we review existing style transfer methods, and categorize available style transfer methods into three groups and compare them with details: Probability statistics-based, con‐ tent-based, and neural network-based. We also discuss three major types of map style transfer methods: Image to map, remote sensing imagery to map, and image to relief shading. And we compare the advantages and disadvantages of style transfer with vector and raster maps. Finally, we envision the future research of map style transfer in terms of three possible research questions: How to select reference images, how to evaluate style transfer results, and how to integrate style transfer into map design process. [ABSTRACT FROM AUTHOR]

Spectral information of ground object is an important feature of remote sensing data. The utilization of remote sensing spectral information has experienced the development from black and white panchromatic image to multi‐spectral, hyperspectral and time series images. In recent years, with the development of satellite remote sensing and the accumulation of historical data, a large number of revisit observation data have been obtained. The long time series remote sensing data integrate spectral, temporal, and spatial information and can avoid the phenomenon of different object with the same spectra characteristics and same spectrum with different objects to some extent.But there is still no unified concept to describe it.This paper proposed the concept of spectrotemporal based on the concept of spectrum, constructed the theoretical system of spectrotemporal, and discussed the theoretical methods and key technologies of spectrotemporal in detail. First, this paper reviewed the traditional time series change detection methods of remote sensing and analyzed the advantages and disadvantages of the traditional change detection methods.Then, the theory and methods of spectrotemporal were expounded, and the key technologies of spectrotemporal in data organization and storage, data reconstruction, feature depth mining and data source were introduced. The methods of classification and change detection for typical crop based on spectrotemporal information were also proposed. Finally, the research of spectrotemporal change detection is prospected. [ABSTRACT FROM AUTHOR]

Objectives: As an extension of human perception in space and time, remote sensing is a new medium that conforms to the concept of omnimedia. Methods: Based on the analysis of the current situation at home and abroad, this paper proposes the interdisciplinary direction of remote sensing journalism. The combination of remote sensing and journalism has advantages in breaking through the limitations of human vision in time, space, and spectrum, realizing multi‐scale revealing laws and ensuring authenticity. Results: This paper explains that the scientific problems of remote sensing journalism are to build a news mission‐driven remote sensing chain service model and to create a narrative mechanism and lens language system. And it also explains that the essential technical problems of remote sensing journalism are the quality assurance, the realization and the expressiveness of remote sensing data. Conclusions: Combined with the technical applications of Xinhua News Agency in the production of remote sensing news, this paper reveals the outstanding performances including efficient image acquisition, convenient and intuitive forensics, time‐series image extraction, and lens language innovation in current remote sensing news dissemination. [ABSTRACT FROM AUTHOR]

Objectives: The scale of buildings and their distribution is key indicators to measure the economic and social development of a region. Therefore, it is significant to study the extraction of buildings based on remote sensing images. Existing neural network methods still have shortcomings in the completeness of building extraction and the accuracy of building edges. To solve the above problems, this paper proposes a multi-level feature fusion network (MFFNet) based on high-resolution images. Methods: Firstly, we use edge detection operators to improve the ability of the network to recognize the boundaries of buildings. Secondly, we use a multi-path convolution fusion module to extract building features from multiple dimensions, and introduce a large receptive field convolution module to break through feature extraction. The process is limited by the size of the receptive field. After fusing the extracted features, the convolutional attention module is used to compress them, and the global features are further mined by pyramid pooling, so as to achieve high-precision extraction of buildings. Results: The current mainstream UNet, pyramid scene parsing network (PSPNet),multi attending path neural network (MAPNet) and multiscale-feature fusion deep neural networks with dilated convolution (MDNNet) are used as the comparison methods, and we use Wuhan University Aerial Image Dataset, Satellite Dataset II (East Asia) and Inria Aerial Image Dataset as experimental data for testing. Compared with the other four methods, MFFNet improves intersection over union, precision, recall, F1-score and mean average precision by 1.53%, 2.65%, 2.41%, 3.32% and 1.19% on average, achieves a better effect. Conclusions: MFFNet not only accurately captures the detail features of buildings, but also strengthens the extraction and utilization of global features. It has better ex‐ traction effect on large buildings and buildings in complex environment. [ABSTRACT FROM AUTHOR]

Objectives: Intelligent interpretation based on high-resolution remote sensing is an important way to realize the fine generation and rapid update of geographic information. Based on the background of fine-accurate geographical application using remote sensing, this paper analyzes the limitations of remote sensing information products in production and application. We explain the necessity of remote sensing serving geographical research and the key of coupling the fine shape of the map and the accurate content of the spectrum. Methods: Based on the basic understanding that geography guides the research of intelligent remote sensing, we first put forward the development direction and technical ideas of high-resolution remote sensing geoscience analysis based on fine geographical scenes. Then, we propose an intelligent computing mode via the space-time/satellite-ground collaboration. Furthermore, we take the evaluation of rocky desertification cultivated land in Guanling County, Guizhou Province, China as an application case. Results: Through this case study, three basic models, namely zoning-stratified perception, spatiotemporal synergistically inversion and multi-granular decision-making, are used to show how to carry out fine-accurate application by high-resolution remote sensing collaborative computing in complex mountain areas from a comprehensive perspective of refinement, quantification and topicalization. Conclusions: Combined with the previous work experience and practical cognition, several key scientific problems are discussed, such as spatial expression using irregular grids, multimodal reconstruction of temporal features, multi-source un‐ certainty analysis and iterative optimization guided by uncertainty. We give some potential study directions and research ideas in order to establish a more complete and feasible theoretical system and explore the development paths for the research on intelligent remote sensing under the guidance of geography and the fine-accurate geographical application with remote sensing data. [ABSTRACT FROM AUTHOR]

Objectives: Shadows in high-resolution remote sensing images will cause objects information loss and image quality decline, which is not beneficial for relative applications. Current shadow compensa- tion methods often take advantage of non-shadow information around the shadow area to increase the bright- ness, whereas there is an unsolved problem that the contrast cannot be enhanced well enough and self-adap- tively. Wallis filter principle has been used in image dodging. However, when it is used in shadow compen-sation, contrast improvement is not as good as other methods, leading to poor compensation results. There- fore, an improved Wallis model compensation method is proposed in this paper to enhance the brightness and contrast better to restore the shaded information. Methods: First, by adding compensation strength and stretch parameters, an improved Wallis model is designed. The strength parameter is positive to the bright-ness and contrast, and the stretch parameter is sensitive to the contrast. Therefore, the improved Wallis model is more efficient to adjust brightness and contrast. Moreover, an automatic parameters calculation strategy is further explored to customize a suitable compensation model for each shadow area. On one hand, the brightness average and deviation of the adjacent non-shadow region are calculated and used as the compensation target values. On the other hand, based on searching the same kinds of points around shadow boundaries, a series of non-shadow and shadow feature points are matched. Assumed the feature value of the non-shadow point is the approximate value of its responding shadow point, they can be used to calculate strength and stretch parameters automatically. Lastly, the brightness of each pixel in different shadow re-gions will be compensated by customized models to cover the shaded information self-adaptively. Results: In this paper, three sets of comparative experiments are set up to analyze and compare the part compensa- tion method's algorithm, original Wallis method in three images with ordinary object shadows and cloud shadows, respectively. The differences in brightness average and gradient average between the compensated value and the non-shadow target value are used to evaluate the compensation quality. The experimental re- sults show that: (1) Original Wallis model is useful to enhance brightness to some extent, while it is insuffi- cient to improve contrast. As a consequence, the visual compensated results are not acceptable as the other two methods. (2) Part compensation method can improve brightness and contrast effectively. However, it cannot adjust self-adaptively according to each shadow region's condition, leading to some over-compensated and some insufficient compensation results in the same image. (3) The proposed method results indicate the best compensation quality in different shadow regions because the improved Wallis model is more pointed to improve contrast and the automatically calculated parameters values are suitable to customize the com-pensation model for each shadow region. Conclusions: Aiming at the contrast improvement of the current algorithm of automatic shadow compensation, an automatic compensation method based on an improved Wallis model is proposed in this paper. The experimental results show that the newly designed model is more effective to enhance brightness and contrast, which is useful for finding suitable parameters values. Combining with the strategy of automatic parameters calculation, its customized model can adaptively com-pensate each shadow. However, it should be pointed out that there are still some limitations about the shadow border compensation and the internal difference in one shadow area, which need to be further studied. [ABSTRACT FROM AUTHOR]

The construction of space⁃based information service system integrating positioning, navigation, timing, remote sensing and communication (PNTRC) will provide a powerful guarantee for cross⁃border realtime communication, global tracking of moving targets, and rapid response to disasters. It also puts forward new requirements for efficient network communications, especially satellite routing planning algorithms. In order to optimize the communication link and reduce the time delay, this paper makes full use of the advantages of direct view between spatial grids and high coding calculation efficiency, and pro⁃ poses a satellite grid space grid computing method. This paper introduces the GeoSOT⁃3D grid subdivision model and applies it to the aerospace, proposes an aerospace grid index big table, and develops a set of satellite visibility analysis and inter⁃satellite routing planning by querying the grid visibility look⁃up table. In order to verify the feasibility and applicability of the proposed algorithm, based on the coding generation principle and visualization of the aerospace grid coordinate test platform, a global grid index big table is established. Three sets of verification work are carried out after simulating the 90/15/2 Walker constellation, including grid big table experiment, inter⁃satellite calculation efficiency experiment, and inter⁃satellite route planning experiment. The experiments verify the feasibility of the inter⁃satellite link calculation and application process in terms of organization, storage, calculation and planning. The efficiency of grid visibility analysis to determine whether the satellite connection grid code is negative is 2.2 times higher than the traditional algorithm, and the efficiency of the visibility analysis based on the pre⁃established universal visibility table is 20.9 times higher than the traditional algorithm. The efficiency of grid ⁃ based inter⁃satellite routing planning is improved by nearly 25 times under the shortest distance constraint, and approximately 20 times under the minimum hop count constraint. Through theoretical analysis and experimental verification, it is initially shown that the algorithm is feasible and efficient. We hope that the proposed algorithm can be used in emergency communications, disaster warning, maritime rescue, etc., and also contribute to the construction of satellite Internet. [ABSTRACT FROM AUTHOR]

Building footprint polygons have been used in a wide range of aspects in many fields. However, they are too detailed in many applications to meet the production requirements. Aggregating building foot⁃ print polygons can remove unnecessary details while preserving the overall structure and visual impression of them. Nowadays they are usually generated from high resolution cadastral and remote sensing data, having many topological problems. These topological problems will influence application of the data, then bring new requirements to buildings aggregation in automated cartographic generalization. So we should solve these new problems. This paper proposes a method to aggregate building footprint polygons automatically, which can remove these topological problems. This method can effectively maintain the consistency of spatial topological relationship among building footprint polygons, and make three dimension visualization of independent buildings more convenient. And a desktop application to implement our proposed method in this paper. Comparison and analysis of experimental results with results from polygon generalization tool of ArcGIS verified that the proposed method is effective and practicable. The results show that our method has following advantages: (1) This method can remove topological problems while aggregating building footprint polygons. (2) Our classification of triangles is beneficial for aggregation of building footprint polygons. (3) This method can acquire overall building contour conforming cognitive knowledges and independent building footprint polygons with topological consistency at the same time. [ABSTRACT FROM AUTHOR]

Objectives: A method of deep learning change detection with domain knowledge as an optimization strategy was proposed to improve the change detection precision of high-resolution remote sensing images. Methods: The improved change vector analysis algorithm and grey-level co-occurrence matrix algorithm were used to obtain the spectral and texture changes of images, and reasonable thresholds were set to divide the changed samples from the unchanged samples based on the spectral and texture change intensity maps. The pattern shape index and spectral knowledge in domain knowledge were introduced as an optimization strategy to filter the changed samples for obtaining high-quality training samples. The deep belief net⁃ work model was constructed and trained, and the results of deep learning change detection were optimized by the optimization strategy to reduce the influence of “salt and pepper noise” and false change zones on the detection accuracy. Results: The Results of change detection experiments show that the accuracies of Gaofen⁃2 and IKONOS images were increased by 7.58% and 14.69% and the recall by 17.08% and 23.87%, respectively, while the false alarms and were decreased by 30.22% and 23.30% and the missing alarms by 17.08% and 23.87%, respectively. Conclusions: Compared with the method before the optimization strategy was adopted, the proposed method in this paper can effectively improve the precision of change detection, and it provides a new way of using remote sensing images to improve the precision of deep learning change detection. [ABSTRACT FROM AUTHOR]

The variation of the azimuth to Doppler center frequency of the terrain observation by progressive scans (TOPS) mode leads to the relative distortion of the phase of the interferogram. Therefore, high registration accuracy is required for interference processing. The Canadian Synthetic Aperture Radar Remote Sensing Satellite (RADARSAT‐2) TOPS mode image is less useful due to poor orbital accuracy and geometric registration accuracy, which makes the interference registration more complicated than it in sentinel SAR remote sensing (Sentinel‐1) satellite TOPS mode. In order to perform the interference processing of the TOPS mode data, this paper uses the cross‐correlation registration to correct the geometric registration results, and then uses the spectral diversity for the second geometric registration correction, and finally uses the enhanced spectral diversity to implement the final azimuth offset correction. Typically, the Doppler centroid estimation from satellite ephemeris parameters and focusing imaging parameters alone does not provide sufficient accuracy. Therefore, a phase increment method is proposed to optimize the Doppler center estimation and the estimated Doppler center is verified by the image bearing power spectrum in this paper. [ABSTRACT FROM AUTHOR]

Objectives: With the development of remote sensing technology, the spatial resolution of remote sensing image keeps improving, which brings both opportunities and challenges for the traditional remote sensing image classification and change detection. In order to improve the reliability of change detection of high resolution remote sensing image, this paper proposes a method of change detection of remote sensing image based on fuzzy comprehensive evaluation. Methods: Firstly, images of two‑phases were overlapped to a new image which can be segmented at multi‑scale. Secondly, a fuzzy comprehensive evaluation model was established for objects in certain scale to calculate membership of pixels in each object, which took the spectral and texture characteristics of two‑phase remote sensing image objects into comprehensive consideration. Finally, the entropy method was used to fuse the fuzzy evaluation membership degree of each pixel in different scales based on information entropy. Results: Taking two groups of high resolution images with different phases as examples, we realized the effective fusion of multi‑scale change detection based on fuzzy logic, which made full use of multi‑level pixel features and consequently improved the overall effect of single scale object‑oriented change detection. Conclusions: The proposed method provides a new idea for the exploration of multi‑scale change detection. [ABSTRACT FROM AUTHOR]

Objectives: On 14th and 15th January 2022, the Hunga Tonga‑Hunga Ha̓apai (HTHH) underwater volcano in Tonga in the South Pacific Ocean violently erupted and caused a tsunami, which has attracted widespread international attention. In this paper, we attempted to rapidly investigate this oncein‑a‑millennium massive event using integrated remote sensing techniques. Methods: We comprehensively used multi‑temporal satellite optical images, radar images, global navigation satellite system (GNSS) observations and other datasets to quickly interpret and analyze the volcanic eruption process and its impacts. The damage assessment in parts of Tonga was also performed. Based on the integrated remote sensing techniques, we presented a technical framework for the rapid interpretation and analysis of volcanic eruptions, which comprises of six components, namely multi‑source data acquisition, geomorphology monitoring, deformation monitoring, environmental response detection, damage assessment and recovery decision. Results: Our results show that the HTHH underwater volcano in Tonga had exhibited obvious surface displacements in the satellite radar line of sight direction with a maximum accumulated displacement of 6.0 cm since June 2020, and erupted since around 22nd December 2021. During the volcanic eruption, the local magnetic field and ionosphere exhibited obvious abnormal signals, and the continuous GNSS data in Tonga suggested clear surface displacements with an uplift up to 50.2 cm. Most areas of Tonga were blanketed in ash, but it appeared that, in northern part of Tonga̓s capital, little damage was observed in the coastlines and the main buildings and roads remained intact. Conclusions: This study shows how to use integrated remote sensing techniques to quickly respond to underwater volcanic eruptions, including the topography evolution of the HTHH volcano and the damage assessment of its eruption. It is believed that the remote sensing integrated technical framework shall not only contribute to the speedy recovery and reconstruction of the society after the HTHH volcanic eruption, but also to the mitigation of future geohazard events. [ABSTRACT FROM AUTHOR]

Objectives: With the enrichment of heterologous image acquisition methods, heterologous image is widely used in many fields, such as change detection, target recognition and disaster assessment. However, matching is the premise of heterologous image fusion application. Simultaneously, due to the differences in imaging mechanisms of different sensors, heterologous images are more sensitive to differences in illumination, contrast, and nonlinear radiation distortion. Therefore, heterologous image matching still faces some problems. There are two main problems, heterologous image feature detection is difficult due to the difference of imaging mechanism, which indirectly increases the difficulty of matching, heterologous image has significant differences in illumination, contrast and nonlinear radiation distortion, which reduces the robustness of feature description and easily leads to matching failure directly. Methods: This paper proposes a new matching method considering anisotropic weighted moment and the histogram of the absolute phase orientation. Firstly, anisotropic filtering is used for image nonlinear diffusion. Based on this, the maximum moment and minimum moment of image phase consistency are calculated, and the anisotropic weighted moment equation is constructed to obtain the anisotropic weighted moment map. Then, the phase consistency model is extended to establish the absolute phase consistency orientation gradient. Combined with the log polar description template, a histogram of absolute phase consistency gradients (HAPCG) is established. Finally, the Euclidean distance is used as the matching measure for corresponding point recognition. Results: Several groups of heterologous remote sensing images with illumination, contrast, and nonlinear radiation distortion are used as data sources of experiments with scale invariant feature transform (SIFT), position scale orientation -SIFT(PSO -SIFT), Log -Gabor histogram descriptor(LGHD) and radiation variation insensitive feature transform(RIFT) methods, respectively. The results show that HAPCG method is superior to SIFT, PSO-SIFT and LGHD in the comprehensive matching performance of heterologous remote sensing images, and the average matching number of corresponding points is increased by over 2 times, and the root mean square error is 1.83 pixels. When compared with RIFT method, HAPCG method can achieve higher matching accuracy in the case of similar corresponding points and can realize the robust matching of heterologous remote sensing images. Conclusions: The proposed HAPCG method can achieve robust matching performance in heterologous remote sensing images and provide stable data support for multi-source image data fusion and other tasks. [ABSTRACT FROM AUTHOR]

Objectives: Ship detection in large-scale, high-resolution optical remote sensing images, especially in visible spectral remote sensing images, plays an important role in both military and civilian fields. It has always been an on-going and challenging research topic in the past decades, due to the complexity and variability of the backgrounds, the multi-scale, multi-type, and multi-posture diversity of ship targets. Methods: In order to promote the development of ship detection based on high-resolution optical remote sensing images (HRORSI), this paper provides an overview of existing study on ship detection methods of HRORSI over the past decades. Firstly, we introduce all the existing monitoring systems for ship detection, but subsequently focus only on HRORSI. Then, a number of topics have been discussed, including the methodology system, the development history, the recent state of the art detection methods, detection datasets and metrics. Results: It is shown that current ship detection methods based on deep learning in HROR-SI, have greatly expanded the adaptability of the complexity of the detection scene and the variation of the target distribution, the speed and accuracy of the results have been greatly improved. It is also found that different influence factors make a big difference in choosing the corresponding ship detection models. Conclusions: Existing methods from different perspectives such as extracting richer features, multi-level and multi-scale feature fusion, more accurate target locating, scale aware ship detection, have made vigorous performance. However, there is still a big gap between efficient and intelligent ship interpretation in actual complex applications. We suggest that the future ship detection methods should adaptively support a variety of backgrounds, scales and optical sensors, the detection model can be effectively transfer to out-distributed target domain scenarios as well as resource-constrained scenarios, and more in-depth target recognition capabilities. [ABSTRACT FROM AUTHOR]

Taking Landsat8 OLI（operational land imager）as the remote sensing data source, the secondary survey of forest resources and the census of geographical conditions as the main supplementary data, this paper carries out the inversion and estimation of aboveground biomass (AGB).Taking the natural forest in Jinzhai county, Anhui province as the research object, the quantitative inversion model of AGB is established by calculating the spectral, texture and topographic features of Landsat8 OLI covering the study area.By using the second class survey of forest resources, the general survey of geographical conditions and field survey data, the influence of different features on AGB estimation is analyzed.Experimental results show that the relative error between the measured value and the estimated value of the optimal inversion model is 0.708 718 and root mean square error is 1.318 983 based on the forest aboveground biomass obtained by the method adopted in this paper. According to the model, the total biomass of Jinzhai county is 4 723 728 530 t, and the results are in line with the actual situation.The quantitative inversion of AGB and the land method adopted in this study are available for large-scale monitoring of forest resources. [ABSTRACT FROM AUTHOR]

Objectives: Mine environmental problems are intensifying, and the environmental monitoring and evaluation of the mining area is an indispensable part of mine management. Previous researches have obtained effective results by evaluating the mining ecological environment through the remote sensing eco-logical index (RSEI). However, the calculation does not take into account that the environmental influence is regional under natural conditions of RSEI, and the geo-graphical location of the mining area has different effects on its surrounding ecological environment. The main purpose of this research is to propose a new re- mote sensing ecological index that is more in line with the ecological response mechanism based on the charac- teristics of the ecological environment of the mining area. Methods: Aiming at the complex research area of large area feature type, this paper presents an improved ecological index named moving window-based re- mote sensing ecological index (MW-RSEI) which based on RSEI and moving window evaluation unit. MW -RSEI divides the ecological environment into four influencing factors: Greenness, wetness, dryness and heat. Each pixel in the study area is taken as the research object, and the extended window of the pixel is taken as the calculation range of each factor. The experiment assigns weight to the center pixel of each win-dow through principal component analysis. Results: The MW-RSEI and RSEI models were used to calcu- late in the study area, the results show that the mean value of MW-RSEI and RSEI in the study area were 0.522 and 0.459, the greenness and wetness had positive effect on the environment, and the dryness and heat had negative effects. Based on the MW-RSEI model, mine geomorphology destroys the regional eco- logical environment grade is worst and poor accounted for 85.40% of the overall area. MW-RSEI in line with the first law of geography. Its results are consistent with RSEI, and it shows more gradient informa-tion of the ecological environment around the mining area, which confirms the theory of dissipative struc-ture in the ecological environment. Conclusions: MW -RSEI refines the local characteristics of the ecological environment, and it can provide an effective basis for ecological environment monitoring and evaluation in mining areas. [ABSTRACT FROM AUTHOR]

Objectives: Change detection is a process of recognizing the state changes of ground surface by multiple observations. With the improvement of image data quality, it provides more possibilities for people to realize change detection. Traditional multi- temporal remote sensing image change detection is easily af- fected by the season in which the images were taken, solar altitude angle and shooting angle etc. In addi-tion, the traditional calculation methods of change index, variables which susceptible to the interference of change object are often introduced, such as mean value, median value and so on. To solve such problems, we proposed a change detection method based on vector data. Methods: Firstly, since traditional change de- tection method is always influenced by shooting season, shooting angle, etc, we use a vector-image method, which is different from the previous image-image method. With the application of this method, the change differences between the old and new images were calculated by using a similarity measurement method, and the change object will identified by a threshold value. The vector-image method using the vector data and the new time image data detect the changes. Secondly, the anomaly detection method in the data mining method can be introduced into the change detection method, hence the outlier has a fewer and different characteristics compared with the normal object. In this paper, we firstly got the object through incremental segmentation under the constraints of the previous vector image, then extracted its texture and spectral fea- tures to get the dataset by the principal component analysis transform. After that, using isolation forest method to calculate object's change index, we obtained the change threshold by Bayes method. Results: We took two experiments, and the effectiveness of the proposed method was verified by comparing image-image and vector-image change detection methods, as well as Markov distance and isolation forest change methods, among which, the accuracy rate of experiment 1 is 0.923 5 and that of experiment 2 is 0.931 8. Conclu- sions: By comparing the image - image method with the vector - image method, the experimental results show that the proposed method can not only improve the accuracy of change detection, but also improve the automation and intelligence of image-based change detection. [ABSTRACT FROM AUTHOR]

3D reconstruction technology is widely used in digital elevation model production, robot naviga‐ tion, augmented reality and autonomous driving, etc. Disparity map is an important expression of 3D recon‐ struction, and stereo matching is the most widely used technology to obtain a disparity map. In recent years, with the development of hardware, data sets, and algorithms, stereo matching methods based on deep learning have received extensive attention and achieved great success. However, these works are mainly validated in close ‐ range images, and the evaluation on remote sensing aerial images is scarce. This paper reviews deep learning methods for stereo matching, and selects five representative models, such as GC‐Net (geometry and context network), PSM‐Net (pyramid stereo matching network), GWC‐Net (group‐ wise correlation stereo network), GA ‐Net (guided aggregation network), HSM ‐Net (hierarchical deep ste‐ reo matching network), and applies them to a set of open source street ‐ scene datasets (KITTI2015) and two sets of aerial remote sensing image datasets (München, WHU). The various networks are analyzed, and the performance of deep learning stereo matching methods is discussed and compared to traditional methods. The experimental results reveals that most of the deep learning methods exceed the classic semi‐ global matching and had a powerful generalization ability on cross‐dataset transfer. [ABSTRACT FROM AUTHOR]

City is the place aggregated massive human activities. City is the exchange hub of population flow, goods flow, information flow and currency flow, which is highly dynamic and complex. Smart city provides various tools to acquire spatiotemporal big data, such as satellite and drone remote sensing, mobile sensing, social sensing, crowdsourcing sensing, etc., which enable us to sense urban dynamics. This paper introduces the framework of urban dynamic sensing, describes the typical applications of spatial dynamics, human behavior dynamics and space behavior interaction dynamics, and discusses the problems, such as the uncertainty in spatiotemporal big data, the multi ⁃view ensemble learning in urban sensing, the verification of the urban dynamic results and the cascading influence of multi⁃urban factors. Outlooking the future, the study of urban dynamics should combine with real⁃time Internet of things data to sense multi⁃dimensional, multi⁃spatiotemporal resolution urban dynamic to enable refined urban governance and to effectively solve urban problems. [ABSTRACT FROM AUTHOR]

The rapid development of deep learning provides an important technical means for intelligent analysis of remote sensing big data. Firstly, this paper mainly introduces the deep learning modes in remote sensing data recognition and application, and proposes a deep reinforcement learning, multi ⁃ task learning and sub ⁃ pixel ⁃ pixel ⁃ super ⁃ pixel feature learning models for object features recognition from LiDAR point clouds, optical remote sensing images and hyperspectral images. The model parameters are basically ob⁃ tained by learning, and thus the workload of the parameter adjustments is small. The spatial and contextual information, texture and spectral characteristics between ground objects are fully taken into account, so the presented models have good generalization abilities. Then, it describes the progress in terms of the joint deep learning and multi ⁃ source remote sensing data in accurate poverty alleviation assessment, wetland change and spatial analysis in Qinghai ⁃Tibet Plateau in the past 20 years, and corn yield estimation. In or⁃ der to better promote the transformation from remote sensing data to knowledge, it is necessary give full play to the advantages of deep learning in remote sensing big data processing, and develop new data pro⁃ cessing algorithms and technologies. [ABSTRACT FROM AUTHOR]

In view of the existing machine learning methods in the field of high -resolution remote sensing image building extraction and the like, which requires positive and negative training samples to participate at the same time, a one- class building change detection algorithm based on one -class samples without the need for negative samples is proposed. Firstly, it extracts the morphological building index features of the image, and fuse multifeatures with the spectral features. Secondly, based on the one - class classification method proposed in this paper, from the object-based perspective, it gets the object-level building change detection results. Finally, it constructs a new shape feature which is refined to obtain the final building change detection result. Through experiments on multi-source high-resolution remote sensing images, it is verified that our proposed algorithm is robust and has better detection accuracy than existing building change detection algorithms. [ABSTRACT FROM AUTHOR]

Objective: The building collapse is the main cause of casualties and economic losses in all kinds of disasters. Accurate assessment of the collapse of buildings is an important basis for disaster rescue and government decision-making, and plays an important role in the proper placement of the victims, the protection of the lives and property of the victims. Therefore, the rapid assessment of building collapse after disaster is a research hotspot in the field of remote sensing and civil engineering. The main Objective: of this study was to apply the deep learning technology to automatically identify and assess building collapse and structural damage with on-site images. Methods: The building collapse assessment method used in this paper is ResNet (residual network). A ResNet-50 architecture was implemented in TensorFlow, which consists of 5 stages each with a convolution and identity block, 3 convolution layers in each convolution block and 3 convolution layers in each identity block. According to the building damage assessment standard developed by the China Earthquake Administration, the evaluation output of this model is adjusted to three types: Completely collapsed, semi-collapsed, and non-collapse. In the data augmentation process, geometry transformation and blur processing were used to simulate different shooting environments, to reduce the overfitting of the classifier model by enhancing the training image. Through data expansion, the number of training data has been increased by at least 10 times on the basis of PEER Hub ImageNet Challenge open dataset. In the process of transfer learning, the gradient descent algorithm was used to adjust and optimize the parameters of the neural network. The Results: of this method were evaluated by using accuracy and Kappa coefficient. Results: Finally, in the building collapse assessment experiment, the classification accuracy of the assessment model reached 77.23% and the Kappa coefficient was 0.65; in the structural damage assessment experiment, the classification accuracy of the assessment model reached 77% and the Kappa coefficient was 0.693.The proposed collapse classification successfully attains a relatively high rate for true-positives. Overall, this method performed well. This method shows great promise in supporting on-site decision-making for the rapid accurate assessment of building collapse after disaster. Conclusions: A novel automatic classification method for post-disaster building collapse evaluation is proposed,which meets the building damage evaluation standard. The method is demonstrated on a specific example classification focused on collapse classification and structural damage type identification.The experiment Results: show great promise in the rapid accurate assessment of building collapse after disaster. In the future we plan to improve the model to enhance the ability to extract subtle features in the image. [ABSTRACT FROM AUTHOR]

Traditional land-use change prediction methods are usually implemented by serial algorithms or semi-manual methods and they were often inefficient. This paper develops a parallel land-use change prediction method based on cloud lomputing (Cloud-CMLP), the map reduce programming model is used to parallelize and extend the cellular automata(CA)-Markov model. Taking Hangzhou as a study area, the experiments are conducted as follows: ① Efficiency tests are conducted to compare the core algorithms of Cloud- CMLP under the different number of data. ② The Cloud-CMLP method is used to simulate the land-use change in 2013, and the simulated results are compared with the 2013 remote sensing image classification results to verify the validity of Cloud - CMLP method. ③ The land - use change in 2020 is predicted and analyzed by using Cloud-CMLP, and the predicted results show that the land area of urban construction is rising rapidly and mainly come from the conversion of agricultural land. [ABSTRACT FROM AUTHOR]

According to the existing researches of aerosol optical depth （AOD） spatial coverage improve⁃ ment, the spatial⁃temporal correlation of multi⁃source products is not fully considered in the fusion process, and the uncertainty of the estimated results is not considered. This paper proposes an AOD spatial coverage improvement method based on spatio ⁃ temporal random effects （STRE） modeling with the uncertainty constraint. And this method is applied to the spatial coverage improvement of daily Moderate ⁃ resolution Imaging Spectroradiomete （MODIS） AOD products in eastern China from January 2013 to December 2017. The results show that the STRE model AOD spatial coverage improvement method can effectively improve the spatial coverage and temporal completeness of AOD by considering the temporal and spatial correlation of AOD data. And the introduction of uncertainty analysis in the STRE model AOD spatial coverage improvement can effectively reduce the accuracy of the overall coverage improvement results, es⁃ pecially for the region with serious missing original AOD data on time and space. [ABSTRACT FROM AUTHOR]

Satellite resources are generalizations of limited capabilities of satellite-based Earth observation systems to produce remote sensing data to satisfy observational needs. We propose a satellite resource organization model to address the inconsistency of basic units for ground coverage characterization, the time - consuming process of area target access analysis,and the bottlenecks in abilities analysis in satellite coverage. This model provides a new path for improving the efficiency of various analytical methods. Firstly, it relies on the GeoSOT(geographical coordinate global subdivision grid with one-dimension-integer on two to nth power) reference frame, and then the global space - time coverage information of the satellite is calculated and stored in advance into a grid to form a spatial-temporal coverage grid to provide support for subsequent satellite coverage performance analysis or task scheduling. The traditional real-time complex computation process will turn into a process of “update once, search repeatedly” based on the model􀆳s large table. At the same time, the model uses a multi-scale grid system to characterize the satellite coverage area, which solves the problem brought by inconsistent basic coverage units, and also can provide technical support for simplifying the process of satellite cooperative scheduling. Finally, this paper chooses area target access analysis and regional simple coverage calculation as typical applications to verify that the performance im - provement of methods based on this model can be greater than that of the traditional methods. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicle (UAV) remote sensing method, this paper proposes a new ap⁃ proach to monitor the situation for oilseed rape based on multi-temporal spectral analysis. According to the rape canopy structure, this experiment determines the end-member combination of different growth stages and obtains the abundance data with the linear decomposition model. Aimed at the accumulation process of rape yield, we analyze the relation between the abundance data in three growth stages and the final yield, and propose three independent variable schemes to establish the multi - temporal rape yield estimation model. Experimental analysis shows that the combination of abundance and normalized difference vegeta- tion index (NDVI) is more effective than the widely used NDVI. And the optimal abundance combinations are the same under different planting methods. This model is also suitable for different rape planting pat- terns. Experiment results verify the strong stability of the proposed model, and the multi-temporal combina- tion of abundance data and NDVI can improve the effect of yield estimation model. [ABSTRACT FROM AUTHOR]

The West Liaohe River Basin (WLRB) is one of the most sensitive areas to climate change in Chi⁃ na. Here we use the terrestrial water fluxes derived from the Gravity Recovery and Climate Experiment (GRACE) satellites, precipitation dataset, and in situ runoff records, to calculate the actual evapotranspira⁃ tion (ET) in the WLRB based on the water balance equation. We find that during the drought period of 2005-- 2011, the mean annual ET is 350.5 mm, which is 9.8 mm more than the mean annual precipitation during the same time period. This difference can be explained by the groundwater depletion in the WLRB (about 6.8 mm/ a). We also find that the ET products from remote sensing both underestimate the actual ET in the WLRB, compared with the GRACE⁃based ET results. However, the ET simulated by the global land data assimila⁃ tion system version 2.1 (GLDAS⁃2.1) Noah is overestimated. This paper highlights the capability of GRACE to monitor actual ET in the WLRB and to validate different ET products from remote sensing and models. [ABSTRACT FROM AUTHOR]

The generation of initial seamline network is one key step in image mosaicking. The quality of generation has a massive impact on subsequent local optimization. In this paper, a novel method for generating initial seamline network is designed. The overlapping information of effective regions of images is encapsulated in the form of a bit array and assigns to the geometric elements such as points, lines and faces. The design contributes to excavate the spatial relation between overlapping areas and to facilitate the calculation of joint priorities of overlapping areas. The main consideration of seam line generation is no longer subject to the specific shape of overlapping areas, but only the degree of overlap. Therefore, it greatly improves the robustness of seam line generation. In addition, the simple bitwise operation reduces the complexity of algorithm and thus improves the computational efficiency. This method is compared with the area voronoi diagram with overlap method. The experiments show that the proposed method has advantages in robustness and efficiency of seam line generation. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) technology characterized by high monitoring precision, large scale and continuous spatial coverage, has become a powerful means to solve the problems of deformation monitoring in water conservancy projects. To circumvent the difficulties of InSAR time se- ries analysis in deformation monitoring of reservoirs, a systematic research of methodologies and applica- tions is carried out in this paper, considering the complexity and particularity of the application environ- ment. Results from the application in the Guangnan Reservoir reveal that high -precision deformation time series can be extracted, which verifies that it is effective in deformation monitoring of reservoirs through In- SAR time series analysis. Time series InSAR technique has great potential for reservoir safety survey and retrospect of deformation history. [ABSTRACT FROM AUTHOR]

Remotely sensed images are often degraded due to the haze interference during the imaging pro- cess, which greatly reduces their utilization. In order to solve this problem, a spatial-spectral adaptive haze removal method for single remote sensing image is proposed in this paper. Based on the dark prior theory and haze image model, and taking into account the spatial and spectral varieties in the remotely sensed images, our proposed method effectively overcome the difficulties of over-correction on bright terrain and inadequate correction of haze in some wavebands．A bright object index (BOI) is constructed to extract the bright objects with the help of the density segmentation method, and an adaptive correction function is then introduced to refine the misestimated transmittance. Given the influences of atmospheric scattering are wavelength dependent among visible channels, two empirical accommodation coefficients are applied to de- rive the transmittance of the different channels, achieving the adaptive adjustment of processing intensity in different wavebands. Experimental results show that our proposed method can remove the haze completely and yield visually haze-free images, comparing with the other existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nighttime terrestrial radiation fog detection model using time series data of Multifunctional Transport Satellite-2 (MTSAT-2), which addresses the difficulty in nighttime fog detection with mono-temporal remote sensing data. The nighttime fog is firstly extracted by using monotemporal image. To take advantage of high temporal resolution of MTSAT-2, the temporal curves of brightness temperature difference between band 1 and band 4 and the temporal curves of brightness tempera⁃ ture of band 1 are built based on the nighttime fog detection result of mono-temporal image. The land surface were separated from the nighttime fog and the low clouds by the bright temperature difference accumulate characteristic established by the temporal curves of brightness temperature difference. The nighttime fog are detected by combining three temporal characteristics with support vector classification. The three temporal characteristics are the bright temperature change accumulate characteristic, slope match characteristic and frequency domain singularity characteristic, which are established by the temporal curves of brightness tem⁃ perature. The experiment results of two days show that the nighttime terrestrial radiation fog detection us⁃ ing time series data has a higher accuracy than the mono-temporal method. [ABSTRACT FROM AUTHOR]

The landslide is one of the most common geological hazards in nature, the rapid development of unmanned aerial vehicle (UAV) technology and virtual reality (VR) technology provides important data resources and technical support for immersive simulation and visual analysis of the landslide. Firstly, this paper focuses on the dynamic construction and exploratory analysis of VR scene in landslide hazard，then some key technologies such as diversified organization of landslide data， dynamic fusion visualization of VR scene are addressed in detail，thus a VR scene interaction method based on handle ray is proposed. Finally > a prototype system is implemented and a case study region is selected for experimental analysis. The experimental results show that the proposed method can dynamically construct landslide VR scene based on UAV remote sensing data，and can support the user to carry out immersive interaction and disaster information analysis. [ABSTRACT FROM AUTHOR]