Search

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased 1 % to 2 % when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness. [ABSTRACT FROM AUTHOR]

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world's major scientific and technological powers to develop. Although China's satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world's advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN's recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments. [ABSTRACT FROM AUTHOR]

Different satellite images have different positioning accuracy. For example, stereo satellite images have higher positioning accuracy than resource survey satellite images. In addition, for a large number of non stereo satellite images, due to the inability to build a strong triangulation model, it is impossible to carry out block adjustment alone to improve the image positioning accuracy. High precision and high resolution Orthophoto Images are the basis of resource investigation and monitoring and basic geographic information updating. For example, China's third national land survey, national geographic situation monitoring and other national projects require that the survey base map must reach the accuracy of 1:10000 scale, that is, the mean square plane error of points is less than 5m. For most satellite images, a certain number of ground control points need to be deployed to achieve this accuracy. Due to the difficulty of obtaining high-precision ground control points and DEM data in difficult areas, high-precision mapping has always been an unsolved problem, such as Western China. In addition, due to the limited coverage of a single satellite image, to realize the complete coverage of an image in a large area requires the joint application of multiple satellite images. In this paper, the high-precision collaborative geometric processing model and technical method of high-resolution multi-source remote sensing satellite images are proposed. The high-precision collaborative geometric processing of more than ten kinds of high-resolution domestic satellite images is completed by integrating multi-source observation data. An automatic construction method of large-scale block adjustment model of remote sensing images from domestic satellites based on multivariate generalized control network is proposed, including key technologies such as automatic optimization of optimal tie points under different modes, automatic matching of multi-node parallelization tie points, multi-level gross error elimination and so on, which realizes the automatic and stable construction of aerial triangulation model. The test shows that the positioning accuracy of satellite images is better than the accuracy requirements of 1:10000 scale without ground control points, which solves the problem of geometric positioning of 1:10000 scale accuracy in areas where it is difficult to obtain ground control points in the field of Western China. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Litchi has been one of the characteristic fruits in Guangdong Province of South China. Traditionally, the litchi can be inspected manually in the orchard, due to its susceptibility to the weather, diseases, and insect pests. The number of fruits in the litchi tree can be counted to determine the subsequent agricultural operations, such as pouring nutrient solution, or the removal of insect pests. Nowadays, an unmanned aerial vehicle (UAV) low-altitude remote sensing has been a promising way to observe the litchi, particularly for safety, efficiency, cost-saving, and easy operation. But the litchi images taken by the UAV are characterized by the smaller target size and fewer features. In this study, an improved Single Shot MultiBox Detector (SSD) model was proposed to detect the small litchi fruits using multiple feature enhancement and feature fusion (MFEFF). Firstly, 160 litchi images with a resolution of 4000×3000 pixels were collected in the orchard using the UAV. A sliding window of 512 pixels was also applied to the original litchi images, according to the target pixels between 20×20 and 30×30 pixels. As such, 3 590 images of 1 024×1 024 pixels were captured to boost the feature information of litchi, such as the size. Secondly, the data enhancement was implemented to improve the robustness of the model using some operations, including the image flip, color space transformation. Thirdly, the last two convolution layers of Vgg16 were deleted to reduce the unnecessary computation. The Receptive Field Block (RFB) was used on the Conv8 and Conv9 layers, and the feature map of Conv3_3 layer was added for the feature extraction, where much more features of the litchi were expanded the receptive field for the detailed information. The Efficient Spatial Pyramid (ESP) Block was also applied on the enhancement of the shallow features in the maps. Finally, the improved Path Aggregation Network (IPANet) was used for the multi-scale features fusion at the Conv3_3, Conv4_3, and fc7 layers. A Squeeze and Extension (SE) module was also introduced in the first two feature layers, further to improve the detection accuracy. The channel attention network was also used the global information of litchi images to selectively enhance the weight of the litchi channel, but to efficiently suppress the useless feature information, such as green leaves against the background. At the same time, the size and quantity of anchors were adjusted to match the size of the small target litchi. A training detection was carried out to verify the model for the total of 3 590 labeled images, where 3 to 114 litchi targets were set in each image. Among them, 2 907 images were distributed as the training set, 324 as the validation set, and 359 as the testing set. The precision indexes were the mean Average Precision (mAP), recall, precision, and F1-Score (F1). The results showed that the RFB module was significantly improved the detection, compared with the original, where the mAP, Recall (R), Precision (P), and F1 increased by 2.61, 0.48, 1.49, and 1 percentage points, respectively. The IPANet detection was better than that of the feature pyramid network (FPN), where the mAP value increased by 0.44 percentage points. The SE module was better than the Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), indicating the best score of the three modules. The ESP detection was superior to the atrous spatial pyramid pooling (ASPP), in which the mAP, R, and P increased by 2.51, 0.38, and 1.13 percentage points, respectively. Consequently, the MFEFF-SSD had improved the mean average precision by 30.62, 14.58, 44.46, and 15.93 percentage points, respectively, compared with the SSD, Yolov4-tiny, Faster-RCNN, and CenterNet models. Anyway, the MFEFF-SSD model can be widely expected to more accurately and effectively detect the litchi images taken by UAV. This finding can also provide a strong reference for the detection of small target fruits. [ABSTRACT FROM AUTHOR]

With the rapid development of society, the improvement of material level and the current situation of the large-scale population flow in China, the awareness of security is becoming more and more important in people's life. With the rapid development of image processing and computer vision technology, people try to analyze, process and understand the collected video image automatically without human intervention. The intelligent video monitoring system collects video signals of interested objects in a dynamic scene through a camera, and processes and analyzes image information by a computer. Only by establishing a reasonable and effective urban video monitoring management system can government departments find out problems in the first time. The traditional highway monitoring and commanding traffic scheduling system based on GIS, which can obtain road traffic information and conduct traffic scheduling by remote sensing, has the disadvantage of poor effect on traffic scheduling. In this paper, real-time communication technology and computer vision acquisition technology are used to build a city monitoring system. The experimental results show that this method has strong timeliness and good monitoring effect. Compared with the state-of-the-art methodologies, the proposed framework is efficient and accurate. [ABSTRACT FROM AUTHOR]

As an important economic tree species in China, tea trees play a critical role in promoting economic development. However, the expanding area of tea plantations in China over the recent years has reduced the biodiversity of the ecosystem and accelerated ecological problems. Identifying the spatial distribution of the tea plantation accurately is important for the monitoring of the tea plantation as well as the development of the tea industry. In this study, Wangzhai Town, located in the central of Wuyi County in Zhejiang Province, was chosen as the study area, and Gaofen-1 (GF-1) data on December 28, 2017, and all available Sentinel-2 data in 2017 were downloaded to study the tea plantations extraction method. The spectral and texture features of tea plantations are similar with crops on the high spatial resolution remote sensing image which results in the mixture of the tea plantations and crops. The Normalized Difference Vegetation Index (NDVI) time series data can reflect the phenological changes of the tea trees and crops, which can be used to improve the separability of tea plantations and crops. In this study, a phenology-based approach was applied to the NDVI time series to distinguish tea plantations from crops by comparing the similarity of the seasonal changes of NDVI time series. Due to the influence of cloud and fog on the satellite image, the complete Sentinel-2 time series data is difficult to be obtained. Therefore, the high temporal resolution Moderate-resolution Imaging Spectroradiometer (MODIS) reflection product (MCD43A4) in 2017 was used to reconstruct the complete Sentinel-2 NDVI time series data (5 m spatial resolution, 5 d temporal resolution) based on Bayesian Maximum Entropy (BME) spatial-temporal fusion algorithm. Then, the Dynamic Time Wraping (DTW) distance was calculated based on the DTW algorithm to characterize the similarity of NDVI time series between the tea plantation samples and other pixels to be classified. Combining the advantages of GF-1 in spatial details and the high temporal resolution of reconstructed Sentinel-2 NDVI time series in capturing the difference of growth process of tea trees, the tea plantation was identified based on random forest algorithm. Two identification schemes: 1) spectral and texture features based on GF-1; 2) spectral, texture features based on GF-1 and DTW distance were set up to investigate the impact of NDVI time series on the tea plantation identification. The results showed that: 1) the reconstructed NDVI time series data derived from MODIS and Sentinel-2 based on spatio-temporal fusion algorithm is able to capture the seasonal dynamics of different objects, and the DTW distance can capture the difference of NDVI time series data between crops and tea plantations and can be used for tea plantations extraction; 2) the accuracy、 error rate、precision、recall and F1score were 96.91%、3.09%、89.00%、83.09% and 0.86 respectively combining texture and spectral information of GF-1 and Sentinel-2 NDVI time series information, which were 94.72%、5.28%、73.09%、84.61% and 0.78 respectively without Sentinel-2 NDVI time series information. Overall, the results of combining GF-1 and Sentinel-2 time series data performed better than that only based on the spectral and texture features of GF-1. These results confirmed that combining high spatial resolution with high temporal resolution time series remote sensing data is an effective method to improve the identification accuracy of tea plantations. [ABSTRACT FROM AUTHOR]

Wheat is one of the main food sources in China, and wheat yield is very important to China's food security. Lodging is a common agricultural disaster in wheat production. Lodging often causes changes in the population structure of wheat, affects the protein synthesis and nutrient transport of wheat, and greatly influences the quality and yield of wheat. To extract the lodging area of wheat quickly and accurately, this study adopted the Unmanned Aerial Vehicle (UAV) remote sensing platform to obtain the visible light images (including the red band, the green band, and the blue band) and the Digital Surface Model (DSM) images of the wheat canopy at the late stage of grain filling. The Excess Green (EXG) index was also calculated. Fusion images of DSM+RGB as well as fusion images of DSM+EXG were created by ArcGIS image mosaic tools. By using the maximum likelihood method and random forest method, the two feature fusion images were supervised and classified for extracting the wheat lodging area, and the results were also compared with the extracted results only based on the RGB images and the DSM images. The results of using four images to extract the lodging area of wheat were evaluated by the confusion matrix and Kappa coefficient. The results showed that the two methods had the same overall trend of wheat lodging area extraction on four images, and the extraction effect of the maximum likelihood method was generally excellent. Based on the random forest method, the overall accuracy of RGB image, DSM image, DSM+RGB fusion image, DSM+EXG fusion image to extract the lodging wheat area was 77.21%, 93.37%, 93.75%, 81.78%, and the Kappa coefficients were 0.54, 0.86, 0.87, 0.64, respectively. According to comparative analysis, the image data extraction accuracy after fusion of the DSM and RGB parameters was the highest, and the overall accuracy was up to 93.75%, the Kappa coefficient was 0.87. The difference between the two images was improved due to added elevation information. The extraction effect of the lodging area was the worst which was only based on the RGB images with an overall accuracy of 77.21% and a Kappa coefficient of 0.54. The reason was that the wheat with normal plants but yellow leaves were misclassified. Compared with the fusion images of DSM+RGB, the classification accuracy based on the DSM images alone was not much different, indicating that DSM effectively reflected the difference between lodging wheat and normal wheat. It was an effective feature, while the accuracy of the latter was higher than that of the former, indicating that the fusion of unreasonable image features effectively improved the image difference and the extraction accuracy of the lodging area of wheat. In this study, the fusion of different image features preserved more detailed information of the image, which enhanced the difference of image features and made the fused image suitable for image classification. This research had shown that the method of image feature fusion could effectively extract the information of lodging wheat and provide a method for accurate identification of wheat lodging. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]

Many users are now showing strong interest in UAV RS (Unmanned Aerial Vehicle Remote Sensing) due to its easy accessibility. UAVs have become popular platforms for remote sensing data acquisition. In a number of practical and time constrained circumstances, UAV RS data have been widely used as a substitute for traditional satellite remote sensing data. However, airspace-related regulations are far behind the rapid growth in the number of UAVs and their wide applications. Much effort of the network-based UAV RS have been made by the UAV RS group of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR, CAS), which proposed the concept of UAV RS data carrier. UAV RS data carrier refers to UAV RS data platform with task planning, data storage, image processing, product generation and output products for various UAVs. An ongoing effort to create a nationwide UAV RS network in addition to an existing ground observational network is being carried out in China. [ABSTRACT FROM AUTHOR]

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

Turfy soil is a kind of special soil accumulated by undecomposed plants which is detrimental to the engineering. In this paper, particular identified patterns for the turfy soil in the northeast of China was raised including the method of extracting threshold value, block analysis and fuzzy evaluation. And field investigations were undertaken to verify the accuracy of identification by remote sensing, and the correlation of field result and remote sensing result was summarized so as to analyze the regularities of distribution and evolutionary mechanism of turfy soil. The result shows that the combination of extracting threshold value, block analysis and fuzzy evaluation are effective methods to predict the distribution of the turfy soil; with the correlation of membership degree and field result, we can analyze the evolutionary mechanism of turfy soil affected by both nature factors and human activities, which is beneficial for the preservation of the turfy soil and also shows significant environmental ecological benefit. [ABSTRACT FROM AUTHOR]

Land-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season (from 1 September to 31 October) on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer (AVHRR) remote-sensing images on board the Meteorological Operational satellite-A (MetOp-A) satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering (MTMF) algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition (MVC) method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper (TM) images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images. [ABSTRACT FROM AUTHOR]

Dynamic change in soil erosion is an important focus of regional ecological restoration research. Here, the dynamic changes of soil erosion and its driving forces in the Dahei Mountain ecological restoration area of northern China were analyzed by LANDSAT TM remote sensing captured via geographic information system (GIS) technologies during three typical periods in 2004, 2008 and 2013. The results showed the following: (1) a decrease in intensive erosion and moderate erosion areas, as well as an increase in light erosion areas, was observed during two periods: one from 2004 to 2008 and the other from 2008 to 2013. (2) Between 2004 and 2008, the variation in the range of slight erosion was the largest (24.28%), followed by light erosion and intensive erosion; between 2008 and 2013, the variation in the range of intensive erosion area was the largest (9.89%), followed by slight erosion and moderate erosion. (3) Socioeconomic impact, accompanied by natural environmental factors, was the main driving force underlying the change in soil erosion within the ecological restoration area. In particular, the socioeconomic factors of per capita forest area and land reclamation rate, as well as the natural environmental factor of terrain slope, significantly influenced soil erosion changes within the ecological restoration area. [ABSTRACT FROM AUTHOR]

The accurate extraction of burned area is important for biomass burning monitoring and loss evaluation. Environment and Disasters Monitoring Microsatellite Constellation put forward by China has two satellites of HJ-1A and HJ-1B in orbit. Each satellite has two CCD cameras with four bands to meet the need of mapping burned area. In order to evaluate the capability for mapping the burned area using HJ satellite's CCD data, a forest fire occurring in Yuxi, Yunnan Province of Southwest China, was selected to analyze the spectral characteristic in the range of visible and near infrared in this paper. The research of mapping burned area was carried out based on the HJ satellites using three spectral indices (NDVI, GEMI and BAI). The color composite images including NIR band could reflect the spectral change in post-fire vegetation with a higher repetition cycle (2 days, or 1 day in some region) and higher spatial resolution (30 m). Through the comparison with the discrimination index M and extraction accuracy, the BAI has higher discrimination capability than NDVI and GEMI, and the highest M value is 2.1943. The extraction of burned area based on BAI showed higher accuracy, and the highest kappa value is 0.8957. Using HJ satellites, the map of burned area with higher temporal-spatial resolution and higher accuracy could provide the potential for dynamic monitoring and analyzing fire behavior. [ABSTRACT FROM AUTHOR]

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China's economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively. [ABSTRACT FROM AUTHOR]

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. [ABSTRACT FROM AUTHOR]

The Yangtze River is the China's longest river and the third-longest river in the world. The river's source region in the Qinghai-Tibet Plateau is especially sensitive to global environmental change because of its high elevation and cold environment. Under the influence of global warming, aeolian desertified land has expanded rapidly in this area. To assess the trends in aeolian desertification from 1975 to 2005, remote-sensing and GIS technology were used to monitor the extent of aeolian desertification in 1975, 1990, 2000, and 2005. The data sources included Landsat multi-spectral scanner images acquired in 1975, Enhanced Thematic Mapper (ETM+) images acquired in 2000, and Thematic Mapper (TM) images acquired in 1990 and 2005. Images recorded between June and October were selected, when vegetation grew well, because aeolian desertified land was more easily recognized during this period. Thematic maps, including land use and geomorphologic maps, were used as supplementary data. Aeolian desertification maps (1:100000) were produced for each year from the Landsat images through visual interpretation. The area of aeolian desertified land increased by 2,678.43 km from 1975 to 2005, accounting for 8.8% of the total area of aeolian desertified land in 1975, an increase of 89.28 km a. Increasing mean annual temperature and the combination of a dry, cold, and windy climate in winter and spring were mainly responsible for the expansion of desertified land. [ABSTRACT FROM AUTHOR]

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods. [ABSTRACT FROM AUTHOR]

A multivalued wavelet transform (MWT) is proposed to fuse multisensor images in feature space. First, feature space is constructed using image-derived features, and then the MWT is introduced. The multisensor images are then fused in the MWT domain using a voting and electing fuser based on the cross-feature scale guideline and the posterior probability of the MWT coefficient. The performance of the MWT is estimated using metric measures regarding various aspects of image quality. A fusion experiment using Thematic Mapper (TM) multispectral and SPOT panchromatic images of south China demonstrates that MWT outperforms smoothing filter-based intensity modulation (SFM) in terms of the fidelity to spectral properties and the injection of salient information. The experimental results confirm that the MWT is a superior fusion method for enhancing spatial quality of multispectral images with their spectral properties reliably preserved. [ABSTRACT FROM AUTHOR]

China frequently experiences natural disasters, of which flooding is the most serious. How to monitor and control natural disasters, assess damage, and provide relief is the most urgent problem for the Chinese government and disaster experts. A national integrated system using remote sensing, geographic information systems, the Global Positioning System, and other technology for monitoring and evaluating flood disasters has been assembled and tried out for 3 years. The system has played an important role in flood mitigation during the trial and has become a key part of the flood management system at China''s National Flood Control Headquarters. This paper presents an overview of the system and its use in China. [Copyright &y& Elsevier]

A hyperspectral image processing and analysis system (HIPAS) has been developed by the Institute of Remote Sensing Applications of the Chinese Academy of Sciences. The HIPAS, built on Interactive Data Language (IDL) and implemented on Windows NT workstations, meets the requirements for the rapid preprocessing of imaging spectrometer data and easy prototyping of algorithms. Integrated with a spectral library, which was implemented on the FoxPro, a popular database environment in the Windows NT platform, the spectral analysis model was established to support hyperspectral image analyses. Based on the HIPAS, some hyperspectraI remote sensing application studies were completed in China. These included mineral identification, agriculture investigation, urban mapping, and the study of wetland vegetation. [ABSTRACT FROM AUTHOR]

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications. [ABSTRACT FROM AUTHOR]