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Multi-epoch interferometric synthetic aperture radar (InSAR) is a highly effective technique for monitoring deformation in urban areas. However, interpreting InSAR deformation can be challenging due to various factors, including inherent geometric imaging distortion, the intricate structure and deformation properties of targets in urban scenes, and the multiple scattering of microwave signals between objects in urban scenes. This paper discusses the challenges involved in interpreting time-series InSAR deformation: (1) Precisely identifying the location of deformation signals and linking them to their corresponding objects, i. e., determining where the deformation signal occurs, (2) understanding the mechanisms and factors that cause the detected deformation signals, i.e., determining what the deformation signal represents, (3) establishing the connection among the detected deformation signals, the deformation events, and the scattering mechanisms. We suggest a parametric framework to improve the accurate interpretation of InSAR deformation. This framework includes several factors, including kinematic characteristics (deformation rate, cumulative deformation, deformation gradient, and deformation model), geometric parameters (position, size, structure, orientation, and roughness), semantic information (land cover type, terrain morphology, texture, and auxiliary information on natural and anthropogenic disturbance) and physical properties (scattering mechanism, penetrability, extensibility, conductivity, and thermal conductivity). Our approach aims to enhance the representation of coherent points for a better understanding of InSAR deformation. This paper offers a comprehensive overview of the advancements achieved in extracting parameters of InSAR coherent points and interpreting deformation based on geometric parameters, semantic information, and physical properties. High-precision 3D positioning is crucial for InSAR fine monitoring in urban areas. It helps determine the source of deformation signals and facilitates the analysis of deformation mechanisms. Semantic information, such as 3D models, high-resolution optical images, laser point cloud data, and land use data, can aid in interpreting InSAR deformation. By combining InSAR deformation data with a deep learning approach, there is an opportunity to interpret deformations effectively. In urban environments, the scattering mechanism of ground objects is complex. Multiple scattering signals can provide effective observations of deformation and information about the target's size. However, combining the scattering mechanism of synthetic aperture radar signals to carry out parameter inversion and deformation mechanism interpretation of urban target terrain remains a challenge. The framework, which considers the geometric parameters, semantic information, and physical attributes of InSAR coherent points, will be crucial for deformation interpretation and mechanism cognition. This framework will enable fine deformation monitoring, intelligent recognition, and application in future urban scenes. [ABSTRACT FROM AUTHOR]

Objectives: In 2015, the Mw 7.8 earthquake in Nepal caused landslides and seriously impacted Nepal and its surrounding areas. Methods: In this paper, the Sindhupalchowk area, which is close to the epicenter of the earthquake, is selected as the study area. First, based on the L-band ALOS-2 and C-band Sentinel-1A synthetic aperture radar data, the detection and identification of post-earthquake landslides are carried out by using stacking-InSAR(interferometric synthetic aperture radar) technology. Second, the two-dimensional time series deformation characteristics of a typical landslide are obtained by combining the ascending and descending orbit data and multi-dimensional small baseline subset-InSAR technology. Results: Combined with optical images, 14 hidden points of landslide are delineated. The main deformation of the typical landslide occurs in the horizontal east-west direction, and the maximum deformation rate is −69 mm/a. Conclusions: By analyzing the trend and periodic term deformation signals in time series of the typical landslide, it is found that the earthquake has a significant acceleration effect on the landslide movement, and the increase of rainfall makes the landslide displacement show a periodic change from August to November every year. This paper can provide a reference for post-earthquake landslide monitoring re⁃ search. [ABSTRACT FROM AUTHOR]

Objectives: Lanzhou New Area is an important window for the development of northwest China. In recent years, the large‐scale construction of mountain excavation and city construction project has led to different degrees of land subsidence, which has seriously threatened the economic and social development of the new area. Methods: Taking Lanzhou New Area as the research object, this paper uses small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) to process 58 ascending Sentinel-1A image data covering the study area from May 2017 to February 2021. The distribution characteristics and spatio temporal evolution of land subsidence in the study area were obtained, and the SBAS-InSAR measurement results were verified by field investigation. Then the related factors affecting land subsidence were discussed. Results: The results show that land subsidence in Lanzhou New Area is mainly concentrated in mountain excavation and city construction area, with an area of about 33.5 km² and a maximum subsidence rate of-68 mm/a. In space, with the development of excavation and filling projects, the land subsidence develops to the southeast of the new district, and the land surface presents a long‐term continuous settlement process. Conclusions: Land subsidence is closely related to many factors such as mountain excavation and city construction project, thickness of excavation and filling, geology and road. Specifically, mountain excavation and city construction project control the spatial extent of land subsidence, and then the filling thickness control the magnitude of land subsidence. The nature of artificial fill is an intrinsic factor causing land subsidence, while road construction will also accelerate the process of land subsidence. The research results can provide scientific basis for the sustainable and safe development of the new district and urban planning and construction. [ABSTRACT FROM AUTHOR]

Objectives: Differential interferometric synthetic aperture radar (D-InSAR) has its own unique monitoring methods in acquiring surface deformation. In view of the difficulties in the application of D-InSAR technology in three-dimensional (3D) deformation monitoring of coal mine surface, this paper proposes a dynamic monitoring method based on fusion of single sight D-InSAR and Boltzmann-Knothe (BK) model. Methods: First, taking into account the characteristics of D-InSAR that it is difficult to obtain large gradient deformations, the Boltzmann function model with good boundary fit was selected, and BK model was constructed by the fusion of the Knothe time function. Second, due to the decoherence of the time-space baseline and the limitation of D-InSAR principle, it can often only obtain the short-term, one-dimensional deformation of the surface in line of sight (LOS). Therefore, based on the projection relationship between the 3D deformation and the LOS deformation, combined with the geological mining conditions of the target area, the fitness function with non-cumulative deformation was constructed. Finally, a dynamic monitoring method of surface 3D deformation of coal mine based on fusion of single sight D-InSAR and BK model was proposed by introducing the fireworks algorithm (FWA). Results and Conclusions: Simulation experiments show that the proposed method can reliably and accurately inverted all dynamic prediction parameters of mining subsidence, and the relative error of the solved parameters was between 0.11% and 7.51%. The subsidence and horizontal movement monitored by this proposed method were consistent with the real values, and the monitoring effect of the PIM-Knothe method was distorted. The results of the anti-error experiment show that under the influence of observation error or model parameter error, the overall effect of the solved parameters by the method in this paper was better, and the ability to resist errors was shown to a certain extent. The proposed method was applied to the 13121 working face of Gubei Mine in Huainan, and the surface 3D deformation of the coal mine from 2019-10-25 to 2019-12-12 was obtained. The conversion of short-term D-InSAR observation to 3D deformation dynamic monitoring based on the proposed method was realized, and the monitoring results were more consistent with the surface movement and deformation measured by the observation station. The reliability and scientificity of the dynamic prediction method for solving the surface 3D deformation of coal seam based on D-InSAR technology has been verified. [ABSTRACT FROM AUTHOR]

Volcano deformation monitoring is one of the most widely studied research topics by using interferometric synthetic aperture radar (InSAR). In the new open and big SAR data era, the development of data processing methods, and interdisciplinary integration have pushed volcanic study into a new stage. This paper reviews the studies of volcano monitoring and parameter inversion based on InSAR. First, we introduce the principle of InSAR, the advanced InSAR time series methods, and the main error sources. Then, we summarize the commonly used classical analytical and numerical models in volcanic deformation modeling. We select several cases to introduce the specific InSAR applications in studying volcanic tectonic dynamic mechanisms including magma transfer, eruption, degassing and hydrothermal activity. Finally, we summarize the current status of volcanic deformation monitoring in China and the future advances of InSAR volcano geodesy. [ABSTRACT FROM AUTHOR]

Objectives: Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in synthetic aperture radar (SAR) image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. Methods: The proposed method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Using this multi-resolution SAR image coastline segmentation dataset, we perform experimental analyses on the effectiveness of the proposed network and the contributions of each module. Results and Conclusions: Experimental results show that the proposed method can work well for the task of sealand segmentation, the overall classification accuracy and mean intersection over union achieve 98.21% and 96.47%, respectively. [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: Multi-temporal interferometric synthetic aperture radar (InSAR) technology is widely used in deformation monitoring over wide areas due to its characteristics of large-scale, high spatial-temporal resolution and the capability to monitor deformation signal with millimeter precision even submillimeter. For the time-consuming problem in acquiring image and data preprocessing over research area, this paper uses looking inside the continents from space with synthetic aperture radar (LiCSAR) products and takes a new time series analysis LiCSBAS method, which greatly improve the computational efficiency. Methods: The quality check and the closure error of the phase loop are used to detect the unwrapping error in the two-dimensional phase unwrapping, and the interferogram with low coherence and the large error pixels are excluded. To minimize atmospheric artifacts, utilizing the generic atmospheric correction online service for In‑ SAR (GACOS) products and based on this, a method for joint estimation of deformation and atmospheric phase is proposed. Taking the Chengdu Plain and western mountainous area as the study area, totally 90 [ABSTRACT FROM AUTHOR]

By using the self-developed unmanned aerial vehicle (UAV) borne mini synthetic aperture radar (MiniSAR) system, all-directional echoes for multiple types of representative ground targets are collected and used for image processing by the Radar Detection and Imaging Techniques Research Group of Nanjing University of Aeronautics & Astronantics (NUAA). A proprietary SAR database for complex targets is constructed, based on which artificial-intelligence-inspired target recognition approaches are studied. To solve the challenging issue of SAR image defocus caused by unstable movement of the UAV platform and the limited accuracy provided by the accessory sensors, novel movement compensation and auto-focus processing methods are proposed. The impact of the image defocus on the classification accuracies provided by the neural networks is revealed, and the limited generalizability of the existing neural networks is demonstrated. Simulation results show that although the classic neural networks, such as AlexNet, ResNet-18, AConvNet, and VGG offer a near-100% accuracy for the MSTAR 10-target classification problem, the 9-class target classification accuracies provided by these networks for the MiniSAR dataset are all much lower than 90%. Since the experiment method employed in this paper closely resembles the practical application scenario, the proposed database will be of great reference value to the development of SAR target recognition algorithms for engineering applications. [ABSTRACT FROM AUTHOR]

This paper constructed a new TerraSAR-X dataset named GDUT-Nansha and then proposed a new lightweight semantic segmentation algorithm using synthetic aperture radar images（ SAR）. It is difficult to apply traditional deep learning models to SAR datasets with small volume due to the tremendous amount of parameters. A revision scheme is, therefore, put forward to deal with such problem. This paper accomplished an improved lightweight convolutional neural network model named revised weighted loss ENet（ RWL-ENet） for SAR images. The current study introduced a weighted loss function to solve the problem of imbalance of training datasets. Compared with other classical convolution neural network models, the efficiency and robustness of the new dataset were validated. Meanwhile, RWL-ENet model attained 0. 884,0. 804, and 0. 645 in terms of three quantitative metrics, including pixel accuracy（ PA）, mean pixel accuracy（ m PA）, and mean intersection over union（ m IoU）. In addition, the parameters of this new proposed model are much less than other classic network models. [ABSTRACT FROM AUTHOR]

Objectives: On 8th January 2022, a large earthquake (Mw 6.7) struck Menyuan County, Qinghai, China, causing serious damage to Lanzhou-Xinjiang high speed railway and forcing the closure of the railway for repairs, which has attracted highly domestic and international attention. Methods: We presented a technical framework to determine earthquake surface ruptures by integrating optical, synthetic aperture radar (SAR) and unmanned aerial vehicle (UAV) images as well as light detection and ranging (LiDAR) data, and evaluated its damage to traffic networks. Firstly, we acquired a range of datasets including GF-1, GF-7, Sentinel-2 optical images and Sentinel-1A SAR images. GF-1 and GF-7 images were used to determine the spatial distribution characteristic of the surface ruptures. Secondly, we employed to estimate 2D surface displacement fields using optical pixel offset technique. One in the east-west (EW) direction and the other in the south-north (SN) direction. SAR pixel offset technique was utilized to acquire surface displacements in the range and azimuth directions whilst interferometric SAR(InSAR) was mainly for surface displacements in the radar line of sight (i. e. the range direction). Structure from motion (SfM) was used to process UAV images to obtain high precision digital surface models (DSMs). Finally, all the abovementioned information was used to precisely determine the spatial distribution and surface displacement characteristics of the earthquake surface ruptures. Results: Our results show that the maximum surface displacement in the EW direction was about 2.0 m, the maximum in the range direction was approximately 1.5 m, and the total length of the surface ruptures was around 36.22 km. Furthermore, we performed an assessment of traffic inefficiency in Menyuan and its surrounding areas based on the distribution of historical geohazards as well as the earthquake surface ruptures using machine learning methods support vector ma‐ chine models. Conclusions: The Menyuan earthquake had the greatest impacts on highways, and the least impacts on rural roads. The southeast sections of the major highways G0611 and G338 had high risks. The technical framework demonstrated in this paper appears to be promising to precisely map surface ruptures, which in turn will directly benefit to earthquake disaster reduction. [ABSTRACT FROM AUTHOR]

Objectives: The temporal‑spatial variation characteristic of the glacier surface velocity is an important indicator of dynamic change of glacier and can provide important information for glacier mass balance. Surface temperature, glacier bed slope and glacier altitude are all important factors that affect glacier surface velocity, and their respective effects need to be analyzed. Methods: In this paper, the offset ‑ tracking method was used to measure the surface velocity of the Gangnalou Glacier, based on high‑resolution Sentinel‑1A synthetic aperture radar (SAR) images ranging from January 2016 to December 2016. We divided the surface velocity into freezing period and ablation period based on whether surface temperature was greater than 0 °C or not. For further analysis, we extracted the central line glacier velocity, and then jointly used surface temperature and digital elevation model (DEM) to analyze the influences of temperature and glacier bed slope. Results: The results showed that surface velocity of freezing period was more stable and central line glacier velocity was faster than that of boundary which tended to 0 m/a. SAR measurements were affected by glacier ablation, accumulation and motion, and showed that the maximum surface velocity was 65.43 m/a during freezing period and 46.28 m/a during ablation period. This surface velocity field showed a partitioning phenomenon in which the velocities of the middle and lower parts of glacier tongue were significantly different from that of the upper part during ablation period. Conclusions: The surface velocity of the Gangnalou Glacier was affected by many complex factors, of which glacier ablation and glacier bed slope are main factors. The partitioning phenomenon in the ablation period could be better explained by increasing surface temperature and changing glacier altitude, which directly affected glacier ablation and accumulation. Additionally, by analyzing the effect of the glacier bed slope, we found that the Gangnalou Glacier followed the compressive and extending flow mode in the freezing period. [ABSTRACT FROM AUTHOR]

This paper proposed a object detection method based on a fused attention mechanism with an improved single shot multibox detector( SSD) algorithm for the detection of synthetic aperture radar (SAR) image ship targets in complex backgrounds by SSD original algorithm that was prone to false detection or missed detection. Firstly, this method introduced the ResNet and improved SSD algorithm to provide rich semantic and detailed information to improve the robustness. Secondly, the fusion of channel and spatial attention enhanced the recognition ability of ship targets and suppressed the interference information such as sea clutter. Meanwhile, it improved the loss function to solve the problem of missed detection when the ships were densely distributed and improve the network training effect. Experiments on the dataset show that the mean average precision ( mAP) of the method is 87. 6%, which is 4. 2 percentage points higher than that of the SSD algorithm, and the missed and false detections of targets are significantly reduced. Compared with the SSD algorithm, this method has better discrimination ability and robustness for ship targets in complex backgrounds, and improves the anti-interference ability. [ABSTRACT FROM AUTHOR]

Objectives: The random volume over ground (RVoG) model is widely used in forest height inversion with polarimetric interferometric synthetic aperture radar (PolInSAR). The model assumes that the forest is a random uniform homogeneous body and the extinction coefficient in the model is a constant without considering the effects of forest vertical heterogeneity and terrain slope. This paper proposes a promising multi⁃baseline (MB) algorithm for forest height inversion based on a slope⁃RVoG (S⁃RVoG) model with linearly varying extinction. Methods: The effects of terrain slope and forest vertical heterogeneity on forest height inversion with the RVoG model are considered in the proposed algorithm. Firstly, the terrain slope is introduced to rectify the effective vertical wavenumber, and the S⁃RVoG model is derived on the basis of the basic RVoG model. Secondly, the linearly varying extinction coefficient, which is assumed to vary linearly with the forest height, is introduced into the S⁃RVoG model, and it can be solved by the Gaussian error function. Finally, MB PolInSAR datasets are used to solve the parameters of the S⁃RVoG model with linearly varying extinction, and the forest height can be obtained by the MB three⁃stage algorithm with coherence separation product criterion. The P⁃band F⁃SAR airborne PolInSAR datasets obtained by the 2016 AfriSAR campaign of the European Space Agency are selected for experimental verification. Results: The results of four MB algorithms, namely MB RVoG, MB S⁃RVoG, MB RVoG with linearly varying extinction, and MB S⁃RVoG with linearly varying extinction, are compared. The root mean square error (RMSE) and the relative error are used to evaluate the accuracy of the obtained forest height. (1) The forest height calculated by the MB RVoG algorithm is a significant overestimation, with RMSE of 6.57 m and relative error of 16.8%. (2) The RMSE of the MB S⁃RVoG algorithm is 5.97 m, and the relative error is 15.1%. The accuracy is improved by about 10% with the addition of terrain slope correction. (3) The MB RVoG algorithm with linearly varying extinction has RMSE of 4.71 m and relative error of 11.0%. Compared with the conventional MB RVoG algorithm, it improves the accuracy by about 28.3%. (4) The RMSE of the MB S⁃RVOG algorithm with linearly varying extinction is 4.27 m, and the relative error is 9.9%. Compared with the results of the MB RVoG algorithm and the MB S⁃RVOG algorithm, the accuracy is improved by about 35% and 28.4%, respectively. Conclusions: The RVoG model is widely used in PolInSAR forest height inversion. The MB S⁃RVOG algorithm with linearly varying extinction considers the effects of terrain slope and forest vertical heterogeneity simultaneously and introduces linearly varying extinction and terrain slope to correct the model, which makes up for the deficiency of the traditional RVoG model. The results show that the S⁃RVOG model with linearly varying extinction performs better in tropical forests with high forest density and great forest height. [ABSTRACT FROM AUTHOR]

Objectives：To address significant geometric deformation and radiometric differences between optical and synthetic aperture radar (SAR) images, this paper proposes an automatic registration method based on spatial geometry constraint and structure feature. Methods：Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the input images. Subsequently, a local geometric correction is performed for the input image by using rational function model, which aims to achieve local coarse registration between the input image and the reference image. Then, the geometric structural feature descriptor is constructed by using orientated gradient information of images, and the feature descriptor is transformed into the frequency domain, the phase correlation is used as the similarity metric to achieve correspondences by employing a template matching strategy. Finally, the least square method is used to eliminate the mismatches based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Results and Conclusions： Three sets of high⁃resolution optical and SAR images were selected as the experimental data. The results the proposed outperform traditional methods in both matching performance and computational efficiency. The proposed method can achieve the registration of optical and SAR effectively and accurately. [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]

High-quality optical data is difficult to acquire in southwest China, due to the cloudy and rainy weather. The synthetic aperture radar (SAR) can work for all-time and all-weather, but the revisit period is usually long. Therefore, it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source, particularly for the survey and assessment on the land resource, as well as the global change. This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region. A Fractal Net Evolution Approach (FNEA) was first utilized to segment the 2016 optical image for an initial segmentation. Then, the second segmentation was implemented using the same approach, where the features of the SAR image were provided for the purpose of guided segmentation. As such, the regions of growth were merged from small to large, in order to eventually form a complete geographical object. A final segmentation was obtained to select the changed and unchanged samples. More importantly, an object contained both changed and unchanged pixels, particularly with a single type of pixel inside the samples. Then, the object-level multidimensional features were extracted using the samples. The initial radar parameters included the backward scattering coefficient, Pauli and Freeman-Durden polarization decomposition parameters. The segmentation was utilized to generate the geographic objects. Meanwhile, some parameters were calculated using the initial parameters, including the mean, and standard deviation, whereas, the texture feature parameters using the Gray-level co-occurrence matrix (GLCM) included the mean, standard deviation, homogeneity, contrast, entropy, and the correlation for a total of 72-dimensional features. The redundant feature dataset was filtered to select the extracted feature set. Consequently, an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection. The inter-sample distance metric learning was used to optimize the separability of feature space. The optimal features were used to calculate the distance between the changed and unchanged samples in different features. Finally, the change detection task was implemented using an object-oriented random forest classifier. The existing change detection pixel-based methods were selected to verify the improved model, including the change vector analysis (CVA), principal component analysis (PCA), multivariate alteration detection (MAD), Iteration multivariate alteration detection (IR-MAD), support vector machines (SVM), and random forest (RF) change detection using radar difference image segmentation. The change detection framework showed a remarkable improvement, with an overall accuracy of 92.90% in the test area. Two representative areas were selected in the study area for sampling and validation. The experiments demonstrated that better performance was achieved than that of the traditional, although the accuracy indexes were degraded in some areas with the complex features. The overall accuracies of the two sample areas reached 95.08 % and 88.16%. The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands, and has certain application value in the monitoring of national land resources. [ABSTRACT FROM AUTHOR]

Objectives: The Sichuan Tibet Railway crosses the Jinsha River specifically the section from Xiongsong Town to Shadong Town, Gongjue County, Tibet Autonomous Region, in which exist strong topography variations and many potential geohazards, and hence it urgent to detect the potential geohazards in this region to ensure the railway safety. Methods: In this paper, 61 Sentinel -1 ascending, 53 Sentinel -1 descending and 7 advanced land observing satellite 2 (ALOS 2) ascending images are used to derive the annual mean deformation rates in the satellite radar line of sight (LOS) with two advanced InSAR (interferometric synthetic aperture radar) approaches, namely generic atmospheric correction online service for InSAR (GACOS) assisted InSAR Stacking and LiCSBAS. The three LOS annual mean deformation rate maps are then used to determine 2D surface movements (one along the slope and the other perpendicular to the slope) in the study region. Results: The comparison between GACOS assisted InSAR Stacking and LiCSBAS results show that they agree with each other with correlation coefficients over 0.92 for the LOS deformation rates and over 0.85 for the 2D deformation rates, suggesting the reliability of GACOS assisted InSAR Stacking. The maximum annual deformation rate of 163 mm/a can be observed in the slope direction and seven landslides (A, B, C, D, E, F, G) can be clearly identified, which in turn lays the foundation for future real-time monitoring. Based on the detailed analysis of the seven regions by using InSAR and optical interpretation results, it is found that the seven landslides had relatively obvious deformation. Landslides A, B, C and D are active landslides, which might cause the result of river blocking due to overall instability. At present, landslides E, F and G are still in the stage of slow deformation. Conclusions: This study also find that GACOS assisted InSAR Stacking can effectively remove long band and topographic related atmospheric delay error with the help of GACOS, which has the advantages of being simple, effective, fast, and easy to popularize and apply. GACOS assisted InSAR Stacking technology can be used to quickly identify potential landslide hazards. [ABSTRACT FROM AUTHOR]

Objectives: Yan’an New District has attracted much attention because of its large‑scale mountain excavation and city construction project. Such mega‑scale land creation, rapid urban construction and complex geological conditions has induced wide land subsidence in the region. Methods: In this paper, the temporal and spatial evolution of land subsidence in Yan’ an New District was assessed using small baseline subset synthetic aperture radar interferometry (SBAS‑InSAR) to process the Sentinel‑1A images covering the period from May 2016 to October 2019. Then, the mechanism and evolution trend of land subsidence in the new district was analyzed. Results: Experimental results suggest that the spatial evolution pattern of land subsidence in Yan’an New District is highly relevant with the process of land creation project. The land subsidence area in the new district gradually expanded from the major urban area to the area of forest along with the land creation project, and its area decreased gradually with the passing of time. The temporal deformation evolution of land subsidence in the new district is related to the thickness of the fill, and all exhibit non‑linear subsidence with various velocities. The temporal evolution of land subsidence undergoes three processes of rapid‑slow‑steady. The greater the thickness of the fill, the larger the amount of sedimentation, the longer it takes for the sedimentation to stabilize. Conclusions: The main internal mechanism of land subsidence is the consolidation and compression deformation of the loess fill, which can be divided into three stages: Instantaneous settlement, consolidation settlement, and secondary consolidation settlement. Based on the characteristics of different stages combined with time series deformation, the land subsidence process and development trend can be analyzed. The research results provide a scientific reference for further monitoring and early warning, urban planning, and prevention and control of subsidence disasters in the new district. [ABSTRACT FROM AUTHOR]

：Objectives: In order to improve the accuracy of SAR （synthetic aperture radar） image change detection, this paper proposes a method of SAR image change detection based on variational method and Markov random field fuzzy local information C‐means clustering （MRFFLICM） method.Methods: Firstly, we fuse the logarithmic ratio images and logarithmic mean ratio images to construct the difference image. Secondly, variational denoising model is established to remove the noise from difference images. Finally, the spatial neighborhood information is introduced into fuzzy local information C ‐means clustering method by using Markov random field to improve the clustering performance.Results: Experiments on two real SAR datasets show that the proposed variational denoising method can avoid removing the small change region and effectively suppress speckle noise of SAR image. Conclusions: The MRFFLICM method can effectively improve the precision of change detection, thus enhancing the adaptability of change detection method. [ABSTRACT FROM AUTHOR]

This paper focuses on the detection of gross error in airborne SAR image coordinates during the process of orientation parameter calculation and stereo positioning. Firstly, based on the range Doppler model, a preliminary discussion is conducted to investigate the impact of image coordinate gross error on the error equations. Secondly, after analyzing the necessity and difficulty of gross detection, based on the quasi-accurate detection, a specific calculation scheme and strategy for orientation parameter calculation is proposed. For the first time, quasi-accurate detection is applied to SAR image orientation. Finally, a series of experiments with both simulated data and real airborne SAR images are carried out to validate the proposed scheme and strategy. The experimental results show that the proposed scheme in this paper is able to detect and calculate the gross errors in image coordinates. Compared to the classical least-square estimation usually used in image orientation, a higher accuracy of both orientation parameters and the subsequent stereo positioning are achieved. The research in this paper is particularly meaningful to improve the quality of the SAR data affected by gross errors in situations where control points are not enough. [ABSTRACT FROM AUTHOR]

In order to reduce the multiplicative speckle noise of synthetic aperture radar SAR image. an improved non-local mean (NLM) algorithm is proposed in this paper. Firstly, the coefficient of variation (CV) was used to estimate the SAR image region division threshold and estimate area division factor based on threshold. Thus. the SAR image was divided into texture area and homogeneous area by area division factor. Then. similarity windows of different scales were used to estimate the ESM corresponding to pixels in different regions, and the ESM matrix of each pixel in the search window under the similarity window and the ESM matrix of the center pixel were selected further. Moreover. the two-norm of the ratio of the two corresponding elements were used to construct the similarity measurement parameter that adapts to the multiplicative noise. filter value was obtained by combining the similarity parameter with the adaptive de cay factor dominated by CV. Finally, the non local average weight filtering of the SAR image was realized by different regions with different filtering values. Compared with NLM filtering algorithm based on the mean ratio and the coefficient of variation. the proposed algorithm performs better in that the equivalent numbers of looks increased by more than 0,3% and edge preservation index increased by more than 0.1% [ABSTRACT FROM AUTHOR]

Landslide evolution is a long‐term and complex process. Geo‐hazards have different characteristics in various data, and have certain applicability in different stages of landslide. This paper applies different characteristics and applicability of various data sources in landslide by the multi‐source data fusion method. The deformation and failure characteristics and temporal and spatial evolution law of Huangnibazi landslide during its dynamic evolution from pre‐sliding to mid‐sliding to post‐sliding are studied. The results show that the deformation and failure process of Huangnibazi landslide can be divided into four stages: startup stage, accelerated deformation (accelerated sliding) stage, front expansion (decelerated sliding) stage and gradual stabilization stage. Huangnibazi landslide is a creep ‐ pull ‐ split landslide formed under the influence of self‐weight, rainfall infiltration, earthquake and vibration caused by human engineering activities. The application of various data in different stages of landslide is summarized: interferometric synthetic aperture radar(InSAR) technology, optical image and topographic data are used to determine potential landslide body before sliding. Optical remote sensing image is used to observe the overall deformation and trend of landslide accumulation body during sliding. Global positioning system (GPS) is used to continuously observe local deformation after sliding. Field investigation is used to determine the characteristics of geological hazard body. [ABSTRACT FROM AUTHOR]

Snowpack is an important factor affecting the global energy water cycle,such as radiation balance and water resources,while remote sensing is a key method to monitor the temporal and spatial distribution characteristics of snowpack and its changing trend.Synthetic aperture radar(SAR) has become an important research means in snow remote sensing because of its all-day and all-weather observation capability.In this paper,the theory and technology of snow parameter inversion methods are summarized and analyzed from aspects of the characteristics of SAR and snowpack,snow scattering models,snow parameter inversion based on SAR intensity and phase information.And the current research states as well as the problematic issues in snow inversion using SAR data are pointed out,and the future research direction is discussed. [ABSTRACT FROM AUTHOR]

As an active sensor,Synthetic Aperture Radar( SAR) finds many applications in earth observation.However,the signal transmission and reception are persistently disturbed through the ionosphere layer at L-band and below.Ionospheric effect brings phase and amplitude errors to the radio signal traversed therein,and further exerts severe impact on the SAR mage quality. In this paper,we review the spatial-temporal ionospheric effects on the imaging performance of SAR systems that operate at different orbital altitudes. Three kinds of effects are investigated: the Faraday rotation,the background ionospheric effects and ionospheric scintillation.Compensation approaches for these effects are then summarized.Finally,problematic issues associated with the ionospheric effects are outlined in the context of SAR theory and applications. [ABSTRACT FROM AUTHOR]

Aiming at the serious problem of de - coherence in the forest classification of the C - band synthetic aperture radar im¬age in conjunction with the optical image, a method of averaging coherence coefficient images in multiple periods in winter obtained by coherent imaging of similar time - phase radar images is proposed to effectively suppress the de - coherence phenomenon to obtain co¬herence coefficient images related to forest tree species. This paper uses the Sentinel - 1 backscattering intensity, coherence coefficient and JL101A image to classify the tree species of the moon lake national forest park in Changchun. The results show that when using on¬ly JL101A for classification, the overall classification accuracy is 82. 3% , Kappa coefficient is 0. 79； when using JL101A data and Sentinel - 1 intensity data for classification, the overall classification accuracy is 85.2% , and the Kappa coefficient is 0. 825 ； when u- sing JL101A data, Sentinel -1 intensity data and coherence data for classification, the overall classification accuracy is 87. 8% , and the Kappa coefficient is 0. 855. After using JL101A data, Sentinel - 1 intensity data and coherence data to classify, compared with u- sing only JL101A data, the precision of user accuracy is increased from 59% to 72%. It shows that the optical image combined with the effective coherence coefficient and backscattering intensity of C band synthetic aperture radar image can improve the classification accuracy of forest tree species. [ABSTRACT FROM AUTHOR]

Glacier motion represents a significant reference for the hazard assessment of glacier and glacial lakes.GF-3,as the first civil spaceborne synthetic aperture radar satellite in China,has important advantages in monitoring glacier motion due to its characteristics of all-weather, all-time capabilities and high spatial resolution. Based on five GF-3 images with FS Ⅱ imaging modes, the surface velocities of the Yiga Glacier, located in Nyenchen Tonglha Mountains, are estimated over five time periods using offset tracking technique during November 2017 to March 2018. The results were compared with the offset tracking results of Sentinel-1 images which have a similar time with GF-3 image and based on the assumption that the velocity of the bedrock in the study area should be 0, the velocity residuals of the bedrock in each period are calculated, then the applicability of GF-3 image in monitoring glacier surface motion was evaluated. The results of GF-3 data show that the distribution of the Yiga Glacier motion is similar in four periods, and the maximum surface velocities are all distributed in the central part of the glacier where the elevation changes dramatically. Meanwhile, the results are consistent with the results of Sentinel-1 based on two images. The RMSEs of velocity residuals in the bedrock area in four periods are 1.4 cm/d, 2.0 cm/d, 1.7 cm/d and 2.3 cm/d, respectively, which validate the reliability of the deformation estimated used GF-3 images in this paper. Based on the above analysis, GF-3 SAR data can be used as one of the conventional data sources for monitoring glacier surface movement. Because of its high spatial resolution and high cost performance, GF-3 can play a unique role in monitoring the motions of glaciers in China. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has been proved as an effective tool for agricultural monitoring and its effectiveness depends on the accurate and appropriate interpretation of SAR Information. Stokes pa⁃ rameters, which is proposed on the dichotomy principle of electromagnetic wave, describe the changes of the incident electromagnetic wave affected by objects which are radiated by electromagnetic wave, and then obtain the information from the objects. However, to our best knowledge, few reports focus on crop phenology monitoring using Stokes parameters. This study aims to explore the feasibility of Stokes related parameters for crop growth monitoring. In this paper, Stokes parameters and their subparameters are calcu⁃ lated based on assumption of wave transmitted in horizontal and received in both horizontal and vertical po⁃ larization. Then these Stokes parameters are derived from 5 multi⁃temporal Radarsat⁃ 2 images and aver⁃ aged relying on each oilseed rape field area. The correlation between all of the Stokes parameters and oil⁃ seed rape growth parameters including above ground biomass (AGB), height and leaf area index (LAI) are computed by Pearson product moment correlation coefficient. The significance of these Stokes parameters for oilseed rape AGB, height and LAI inversion are derived from random forest (RF) model. The results in⁃ dicate the potential of Stokes parameter for crop growth monitoring and growth parameters inversion. It demonstrates that scattering power related Stokes parameters reveal better performance for crop AGB in⁃ version, but scattering mechanism related Stokes parameters such as degree of polarization (m), the degree of linear polarization (ml) and the ratio of linear polarization (μl) are more sensitive to crop height and LAI. Moreover, the results suggest that it is necessary to analyze the influence of crop structure on scattering power when crop growth parameters inversion is performed with Stokes parameters. [ABSTRACT FROM AUTHOR]

The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to ana⁃ lyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency’s Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future. [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]

In order to promote the wider use of ground-based synthetic aperture radar (GB-SAR) in the field of surface micro-deformation monitoring, this paper summarizes the main types of GB-SAR and introduces the working principles and important parameters of some representative systems. By taking the S-SAR (slope-synthetic aperture radar) ground-based radar independently developed by the China Academy of Safety and Production Science as an example，the key processing techniques utilized by the GB-SAR are introduced. Experiments verify the accuracy of GB-SAR. It also shows the application of GB-SAR in open pit mine slope monitoring，mine dumping site and landslide emergency rescue. Finally，the problems and suggestions of GB-SAR in monitoring are proposed. [ABSTRACT FROM AUTHOR]

By using Sentinel-1 A ascending and descending synthetic aperture radar (SAR) data，this paper extracts the high-precision interferometric SAR (InSAR) coseismic deformation of the 2016 Menyuan Mw5. 9 earthquake，inverts the fault geometry and slip distribution using simplex method and nonnegative least squares method 9 and constructs the deep geometry model of regional fault zone. The results show that coseismic deformation of the Menyuan Mw5. 9 earthquake is dominated by surface uplift, and the maximums along the line-of-sight direction of ascending and descending tracks are 5. 3 cm and 7.1 cm respectively. The fault strike and dip angles are 133° and 43° respectively. The seismic slip is dominated by thrust component，mainly occurring in 6.14-12. 28 km underground. The maximum slip is about 0. 5 m, the average slip angle is 66. 85°ï¼Œand the earthquake moment is 1. 0X1018 N*m (Mw5. 94). The fitting residual root mean square of deformation observations is 0. 36 cm. The deep geometry of the regional fault zone is characterized by flower structure，which inclines to the south-west as a whole. The Menyuan earthquake rupture is a blind fault which does not appear on the surface in the flower structure. Relevant results can provide the reference for studying regional crustal movement and deformation, active fault and earthquake preparation and occurrence. [ABSTRACT FROM AUTHOR]

Affected by global climate change，most glaciers in southeastern Tibet have deteriorated more and more frequently. The risk of mountain disasters» such as floods, surges and debris flows， has increased sharply in recent years. This paper implements a research on glacial lakes classification by using multi-source synthetic aperture radar (SAR) images. According to the statistical analysis of intensity difference between signals coming from water and non-water objects，a method of glacial lake extraction and dynamic monitoring based on the intensity standardization ratio of sequential SAR images is proposed. Within a typical experimental area in Gongba Glacier basin，the image series of ALOS/PALSAR-1 of Japan Space Agency and Sentinel-1 A of European Space Agency are selected to carry out dynamic extraction of glacial lakes and long-term change analysis over 11 years9 time, just for validation purpose. The temporal and spatial variations of glacial lakes at the end of Gongba Glacier from 2007 to 2018 are successfully obtained. And the further analysis finds out that the volume of glacial lakes increase rapidly in the past ten years. This evidence can also confirm the current situation of aggravated melting of Gongba Glacier. In addition, the recent monitoring results in 2018 show that the expansion of local burst gushes has broken the inherent life cycle of glacial lakes，and even trigger secondary disasters of floods and debris flows. It is necessary to strengthen monitoring and prevention. [ABSTRACT FROM AUTHOR]

Due to the influence of global climate change，most glaciers in southeastern Tibet and Hengduan Mountains in recent years have been losing weight, deteriorating and thinning, which has caused the variation of glacier movement characteristics，resulting in frequent disasters such as debris flows and landslides. In order to break through the bottleneck of optical remote sensing restricted by climatic conditions，this paper combines satellite and ground-based synthetic aperture radar (SAR) technology and selects Hailuogou Glacier (HLG) basin as a typical research area to carry out time series monitoring and analysis. Firstly, by using 38 SAR images acquired by PALSAR-1/2 satellites from 2007 to 2018，the temporal and spatial variations and local surface displacements of HLG in Gongga Mountain are monitored by using the pixel offset tracking (POT) method. The average velocity of HLG No. 1 is slowed down by 7. 27% per year in recent years，and the slow-down rate reaches 15. 57% per year in the ablation areas. At the same time，several unstable landslides are detected by POT and Stacking-InSAR methods at the moraine embankment on the side of the glacier. Statistical analysis confirms that the movement of such landslides is strongly correlated with the melting of the glacier. The sliding speed reaches its peak in summer every year. The maximum sliding speed in 2018 was 100 mm/d in the north-south direction and 50 mm/d in the east-west direction. Subsequently, by utilization of the high-frequency real-time monitoring data of ground-based radar，it is further determined that the sliding speed reaches its peak value of 150 mm/d on July 9，2018，and abnormal fluctuations occur with the subsequent collapse，which shows in detail the whole process of landslide creep to result in disasters. Relevant research data and the monitoring results can provide a reference for the study of the cryosphere and mountain hazards. [ABSTRACT FROM AUTHOR]

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously，which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements，and the geometric distortion introduced by the side-looking orbit. In this paper，from landslide detection and monitoring perspective，the four major challenges of satellite radar technologies are discussed：① The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e. g. S-band or L-band)，a short temporal resolution，and/or a high spatial resolution (e. g. 1 m or even higher)，and/or advanced interferometric SAR (InSAR) time series，and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range splitspectrum interferometry techniques.② Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③ The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data； in contrast，for high-resolution SAR data，a machine learning approach can be used to identify water bodies，shadow and layover areas without a requirement of a high-spatial-resolution DEM.④ Residual topographic phase exhibits in areas with high buildings or steep slopes，which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e. g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development. [ABSTRACT FROM AUTHOR]

In China，traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space~air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region，highlighting these problematic locations； yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence，it makes the compilation of long displacement time-histories feasible，contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then，detailed assessments can be done through the development of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges，validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention [ABSTRACT FROM AUTHOR]

This paper proposes a fast single stage synthetic aperture radar(SAR) ship detection and az­imuth estimation method. It can output the location, type and orientation of the object in the image af­ter a forward process, which is completely end to end for training and inference. This method is based on the single shot detector (SSD). The feature pyramid network makes full use of the high-level se­mantic features and low-level position features? which make the bottom and top layers have class in­formation. This can solve the following two problems： Small targets are easy ignored at the top layer and the bottom layer would predict the wrong class. The loss function reduces the weight of huge number easy classified examples, which can avoid dominating the hard classified examples. This can make the objective function converge better and faster. By adding the new azimuth estimation module, the method can perform the two tasks simultaneously with a small increase in calculation. By the ex­periments on the opened SAR ship detection dataset, we can find that the proposed method can detect ships and estimate the orientation rapidly and accurately. [ABSTRACT FROM AUTHOR]

The theory and method of mining subsidence monitoring based on D-InSAR(differential interferometric synthetic aperture radar) technology is a hot topic in the current field of mine deformation monitoring. At present, three-dimensional monitoring model of mining subsidence has been constructed successfully by some scholars through integrating single line of sight to D-InSAR technology and mining subsidence law. However, the model is only suitable for the researches on mining subsidence of horizontal coal seam rather than inclined coal seam. In view of the above defects，according to the relationship between deformation of line of sight monitored by D-InSAR and three-dimensional deformation, considering the surface subsidence law of inclined coal seam mining, the surface subsidence monitoring method for inclined coal seam based on single line of sight to D-InSAR technology is proposed. The simulation experiment shows that the absolute relative error of the subsidence monitoring method is less than 9. 53%, with an average of 1. 31%. The absolute relative error of the horizontal movement monitoring along North-South (SN) and East-West (EW) is less than 9. 78%，with an ave-rage of 3. 71%, which meets the mining subsidence monitoring accuracy requirements. The surface movement and deformation caused by the mining of the 9310 working face of Nantun coal mine in yanzhou mining area, Shandong province China during 2012-01-27 - 2012-02-07 are successfully monitored by this method in this paper [ABSTRACT FROM AUTHOR]

When using synthetic aperture radar interferometry(InSAR) technology to monitor coal mining sub-sidence, the displacement of radar line of sight(LOS) obtained is the vector superposition of three-dimensional dis-placement of surface points in vertical, north-south, east-west directions. Decomposing the LOS displacement into the surface subsidence and the horizontal displacement along the strike and incline of the working face is the basis for studying the law of mining subsidence and obtaining the parameters of surface movement. At present, the main method of inverting mining subsidence three-dimensional displacement based on InSAR LOS displacement is using three SAR images of different orbiting satellites to establish LOS displacement equations for solving three-dimensional displacement components. According to the prediction model of mining subsidence, the horizontal displacement of surface is regarded as the correlation function of subsidence, and the subsidence and horizontal displacement are calculated from a single SAR image. The LOS displacement is projected into the sub-sidence component through directly ignoring the influence of horizontal displacement. Based on the analysis of the limitations of the above methods and the basic characteristics of the surface subsidence and horizontal displacement of the near-horizontal coal seam mining on the main section of the mobile basin, the geometric equations of the multi-period InSAR LOS superposition displacement on the symmetry point of the surface moving basin was established. The equations are superimposed and differentially processed to derive the function model for LOS displacement inversion three-dimensional mining displacement. During the advancement of the working surface, because of the hysteresis of the surface movement, the center of the dynamic subsidence basin deviates to the side of the open hole. According to the characteristics of the central surface subsidence of the moving basin reaching the maximum and the horizontal displacement approaching zero, the dynamic center position of the surface moving basin is automatically searched based on the superposition and difference of the LOS displacement of adjacent pixels. Thereby, the decomposition of the dynamic three-dimensional displacement of the surface during the advancement of the working surface is realized. In this paper, the above three-dimensional displacement inversion model is implemented based on the platform of Matlab, and the model was validated by multi-phase In SAR data. The inversion results of subsidence and horizontal displacement are in a good agreement with the measured data, which is obviously superior to the method of directly decomposing surface subsidence without considering horizontal displacement. This model can be used to decompose the three-dimensional displacement monitored by InSAR in the process of surface movement stability and dynamic movement in near-horizontal coal seam mining. [ABSTRACT FROM AUTHOR]

The Amery ice shelf (AIS) is the third largest in Antarctica. Its behavior controls the mass balance of Antarctica and affects sea level rise while studies focusing on the calving at the boundary of AIS are few. In this paper, an efficient and automatic algorithm for AIS frontal line detection using Sentinel-1 SAR imagery is proposed. Based on characteristics of SAR (synthetlc aperture radar) backscatters distribution, smallest of constant false alarm rate (SO-CFAR) algorithm and morphological filter is combined to obtain the binary map of the Sentinel-1 SAR imagery. Then, sliding windows and cumulative sum strategies are applied to automatically detect frontal positions and map the ice frontal lines. The sensitivity of spatial resolution and profile resolution is analyzed by bilinear interpolation for image resolution resampling, and affection of floating ice is considered in the experimental analysis section. Experimental results are compared with state-of-the-art algorithms, and accuracies reported that the proposed algorithm can provide an accurate frontal line under one pixel and prove it is suitable for ice surface melting water and calving conditions. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important crops for many countries. It is important to obtain accurate wheat planting area. Optical imagery has been widely used in crop classification and made great progress, but the optical imagery in the critical period of wheat growth is often affected by weather and other external conditions. In contrast, synthetic aperture radar (SAR) is potentially effective data source for wheat mapping because of its all-weather, all-day imaging capabilities. The study site was located in a typical urban agricultural region in Gaochun District of Nanjing, the provincial capital of Jiangsu Province, China, with the central coordinates of 118°52'E and 31°19'N. Six SAR (Sentinel-1A) and 4 optical (Landsat-8) images were obtained from the ESA during the growing period of winter wheat. The objective of this study was to explore the abilities of multi-temporal satellite SAR data, optical images and the combination of both types of images to identify winter wheat in an urban agriculture region with complex planting structures. The investigations were through the following steps: 1) The texture features were extracted from the SAR images using the gray level co-occurrence matrix (GLCM), and the backscatter intensity data were also obtained; 2) A support vector machine (SVM) was used as the classifier to map winter wheat, using the different combinations of the Sentinel-1A derived information and optical images. 3) Then, the classification accuracies for different combinations of SAR variables and optical images were evaluated. The results showed that: 1) In the process of winter wheat growth, the backscatter characteristics changed obviously. In the middle of April, the backscatter characteristics were different between winter wheat and other vegetation in the study area, which could provide the theoretical basis for winter wheat classification. 2) Using single polarization, single-temporal SAR data were difficult to meet the accuracy requirements of winter wheat with complex planting structures, and the classification accuracy of VV polarization was higher than that of VH polarization; 3) Compared with VV polarization, the producer's and user's accuracy of VV+VH combination were increased by 27.3% and 13.6%, respectively. When VV polarization was added to VH backscatter intensity images, the producer's and user's accuracy were increased from 50.3% to 85.7% and from 54.5% to 87.9%, respectively; 4) When texture images were added to backscatter intensity images, the winter wheat fields could be mapped with a satisfactory classification accuracy, and the user's and producer's accuracy could reach up to 94.6% and 93.3%, respectively; 5) The combination of multi-temporal SAR and optical images had the best classification accuracy, and the producer's and user's accuracy were 97% and 98.4% (overall accuracy 95.3%, Kappa coefficient 0.91), respectively. The addition of multi-temporal SAR to optical images allowed us to increase producer's and user's accuracy of winter wheat by 3.7% and 3.8%, respectively. The results in this paper show that the satisfactory classification of winter wheat can be achieved using Sentinel-1A data alone in urban agricultural regions with complex planting structures. In addition, texture features of SAR can improve SAR data classification accuracy. The SAR and optical images can make use of the advantages of information complementation of each other to make up for their deficiencies in crop classification. It is indicated that the SAR data can not only be used as a data source instead of optical images to distinguish winter wheat, but also as a supplementary data source to improve the classification accuracy of optical data. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image target recognition plays an important role in military and civil applications， and is also a challenging problem due to the speckle noise. This paper proposed a new architecture for SAR image target recognition based on convolution neural networks. It was an end-to-end network through combining feature extraction and classifier training in pipeline，by linking multiple basic operation units with hierarchical structural. And it improved the effective of the feature by the feedback of the classifier through back-propagation in deep network. The designed architecture consisted of two operational modules，which was composed by three of the five basic operational units： convolution， normalization，pooling， rectification，and full-connection. It learned the parameters in the network by back propagation. For the public MSTAR database，the proposed architecture achieves an accuracy of 98. 61 % for ten-target classification. Compared with other approaches，it effectively improves the recognition precision of SAR image targets. The proposed architecture can classify SAR targets effectively，and achieves a good recognition accuracy. Moreover，it has module structure and can be implemented without complicated pre-processing. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) permits all-time, all-weather observation and strong penetration, and is widely used in disaster monitoring and evaluation, resource exploration, and military reconnaissance. However, speckle noise seriously affects the quality of SAR images, thus limiting the use of SAR image for quick access to information. In this paper, a new road damage extraction method for high-resolution SAR images based on GIS data and Bayes network is proposed. Guided by GIS data, suspected damaged roads are extracted using the fusion of level-set segmentation and an improved D1 line detection. A Bayes network is applied to further confirm real damaged roads based on multi-evidence and the observed values from suspected damage road, to eliminate the false-alarms from the SAR images. Experimental results indicate that our proposed method can extract road damage information quickly and accurately. [ABSTRACT FROM AUTHOR]

Aim at the problem that the traditional feature extraction methods cannot get the high level structure features, this paper put forward a new soft probability pooling method, which is used in multilayer Deconvolutional Network, then high level structure features can be learned and be used for classification of SAR image. Firstly, the SAR image was divided into patches; then, the feature coding of each patch was obtained by means of multilayer Deconvolutional Networks, which can learn features suitable for image classification in different scale; finally, the SAR image was classified through the features used in SVM classifier. Experimental results on the first batch domestic PolSAR images show that the classification accuracy rate of the proposed algorithm is superior. [ABSTRACT FROM AUTHOR]

This paper proposed a new similarity measure known as cross cumulative residual entropy (CCRE) for multimodal image registration in medical imaging applications. It investigated the use of CCRE for multi sensor registration of remote sensing imagery. In particular, it investigated the extreme case of registering synthetic aperture radar images to optical images. It also proposed a novel extension to the Parzen-window optimization approach which involved applying partial volume interpolation in the calculation of the gradients of the similarity measure. The experimental results show that the proposed approach which uses CCRE as the similarity measure and partial volume interpolation in the optimization procedure provides superior performance to other approaches investigated. [ABSTRACT FROM AUTHOR]