Landslide is a common geological disaster, and rapid landslide extraction using high-resolution remote sensing imagery (RSI) is of great significance for emergency rescue and damage assessment. In RSI, landslides often have irregular shapes, large-scale variations, and are easily affected by environmental factors. Existing deep learning methods have limited ability in extracting multiscale features, integrating these features effectively, and adapting to complex environments, resulting in models that are not optimized for robustness. To overcome these challenges, this study proposes a spatial context-guided calibration network (SCGC-Net) for multisource remote sensing data. SCGC-Net introduces a novel combination of hybrid multiscale feature extraction, context-aware modulation of landslide characteristics, and a progressive feature calibration fusion strategy, enabling efficient feature extraction, accurate feature integration, and enhanced cross-domain generalization when working with multisource remote sensing data. SCGC-Net was tested on several datasets representing diverse geographical regions and imaging platforms, including the CAS Landslide Dataset (CLD), HR-GLDD, Bijie, and global very-high-resolution landslide mapping (GVLM). Experimental results indicate that SCGC-Net outperforms existing methods across all evaluation metrics and exhibits superior generalization performance in domain adaptation experiments.

Facing massive Synthetic Aperture Radar (SAR) data, the challenge of achieving rapid and efficient data processing has garnered attention. Currently, most time-series InSAR processing solutions are designed to operate on a single computing platform, leading to drawbacks such as inflexible deployment and slow data transmission. Leveraging the open-source Kubernetes platform, we have employed cloud-native technology to deploy a cross-platform, multi-level parallelization algorithm across multiple nodes. The algorithm is designed based on modular concept, and its modules can successfully achieve cross-platform deployment by relying on both the Network File System (NFS) protocol and the Common Internet File System (CFIS) protocol. This overcomes the limitations of the previous data file storage system, which solely depended on the NFS protocol, leading to deployment on a single operating system. Deploying the algorithm on different operating systems, our results show that the speeds of Linux platform's algorithm parallel modules were improved 16% and 30%, respectively. The successful cross-platform operation of the algorithm enables the data processing workflow to assimilate the strengths of different operating platforms, enhancing data visualization capabilities and gaining support from diverse platform resources. This introduces a new approach for large-scale time-series InSAR processing.

Monitoring the deformation of retrogressive thaw slumps (RTSs) is important for understanding their evolution and future expansion trends. In this study, we utilized Japanese ALOS-2 PALSAR-2 data from 2015 to 2023 and European Sentinel-2 optical imagery from 2019 to 2023 to conduct long-term time-series evolution and surface deformation monitoring of two RTSs in the south of Mt. Fenghuoshan in the Tibetan Plateau. Analysis of the temporal Sentinel-2 imagery revealed that both RTSs exhibited a noticeable trend of rearward retreat, with annual rates of approximately 10 meters and 16 meters, respectively, from 2019 to 2023. Consequently, their areas increased by 4602 m2and 3513 m2, respectively. Interferometric Synthetic Aperture Radar (InSAR) monitoring results indicated obvious surface subsidence in these two RTSs, with deformation rates reaching -15 mm/year and -9 mm/year. The deformation rates exhibited distinct spatial distribution characteristics, with greater subsidence observed in the slump headwall compared to the scar region. The findings of this study show that headwalls remain unstable, with an ongoing risk of expansion.

With the global warming, thaw slump activity has increased in permafrost regions of Qinghai-Tibet Plateau (QTP), which influence the stability of human infrastructure and carbon cycling. However, the intrinsic dynamic process of surface displacement of the retrogression thaw slump (RTS) is still less understood. Here, we employed the spaceborne interferometric synthetic aperture radar (InSAR) based on L-band ALOS-2 PLASAR2 data acquired from December 2015 to May 2022 to monitor the surface subsidence trends of thaw slump-derived thermokarst in permafrost terrain of Qinghai-Tibet Plateau (QTP). The InSAR analysis reveals the thermokarst subsidence of -81~46 mm/year between 2015 and 2022. A large number of RTS were distributed in the slopes, with the large annual average sedimentation rates. Besides, the time-series seasonal deformation reveals that during the period from January 2019 to March 2019, with the mean temperature below 0 °C , RTS’ deformation still represents large seasonal subsidence, which indicates the intrinsic pattern of surface displacement of the thaw slump is not simple cold-season freeze heaving.

As a global warning, the Qinghai-Tibet Plateau (QTP) permafrost is undergoing degradation, which can be a threat to the high-altitude infrastructure. Multitemporal interferometric synthetic aperture radar (MT-InSAR) is an effective tool to monitor freeze-thaw deformation cycles of permafrost. In this study, we employ small baseline synthetic aperture radar interferometry (SBAS) in the northern Qinghai-Tibet Railway (QTR), including Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions with 73 scenes of L-band ALOS-2 stripmap SAR images between 2015 to 2020. The experimental results show that the maximum deformation of permafrost from Tuotuohe to Beiluhe area can reach -35 mm/year. Besides we combined optical data (GF-1, GF-2) found that the permafrost of some regions is undergoing degradation, which may cause thermal melting landslides. And our results can provide significant insight for monitoring permafrost deformation in QTP.

Phase unwrapping (PhU) is an important step in interferometric synthetic aperture radar (InSAR) technology. At present, difficulties are encountered when using deep learning to solve the PhU problem because the fringe density of the actual interferogram varies, resulting in an imbalanced class of semantic segmentation. Deep learning cannot completely use gradient information, and it is difficult to address a large number of residues. In this letter, a PhU semantic segmentation model based on gradient information fusion and improved PhaseNet network is proposed to solve the problem of imbalanced classification and error propagation. 21 613 pairs of phase samples are constructed by using simulated and real Sentinel-1 InSAR Data. The experimental results show that the average classification accuracy of the method can reach 97%, and the mean square error is only 0.97. The average processing speed of$256 \times256$slices is only 0.5 s. Compared with the traditional methods and other deep learning methods, this method solves the problem of classification imbalance, and the use of fusion gradient information improves the efficiency of the algorithm as well as reduces the burden of network classification and the error propagation, showing increased robustness in the case of many residues and high fringe density.

Synthetic aperture radar (SAR) interferometer (InSAR) is now a key geodetic tool for monitoring the surface displacement. Thanks to ESA's Sentinel-1 sensors with IW mode as its default acquisition mode for land observations and its free access data policy, which have global coverage at moderate resolution with about 20m, national scale InSAR-based deformation is being studied in recent years by using big data techniques such as high performance computing and cloud computing. In this paper, we proposed the time series InSAR technique called Coherent-Scatterers InSAR (CS-InSAR) and its parallel solution for processing the whole CS-InSAR chain of Sentinel-1 data automatically and efficiently, considering the characteristics of CS-InSAR algorithm, such as frequent I/O data flow and heavy computation. By developing the parallelized CS-InSAR algorithm on the Big Earth Data Platform, 11922 satellite SAR data from September 2018 to December 2019 over China were processed, and the preliminary national InSAR-based surface deformation mapping for 2018–2019 was produced, with the deformation accuracy better than 0.6 cm in urban area.

Tanggula Mountain is located in the hinterland of Qinghai-Tibet Plateau (QTP), and the spatial distribution of permafrost has relatively strong heterogeneity. In recent years, permafrost is quickly degrading due to climate warming and human activities. The freeze-thaw cycles of the active layer on the permafrost cause seasonal uplift and subsidence. And it is difficult to accurately retrieve the surface deformation using the Temporarily Coherent Point synthetic aperture radar interferometry (TCPInSAR) in low-coherent permafrost areas, because there are few coherent targets identified in this area. To improve the density of measurement points, an improved TCPInSAR technique, namely Distributed Scatterers and Coherent Targets InSAR (DS-CTInSAR) is proposed in this paper. The Anderson-Darling (AD) test is used to extract statistically homogeneous pixels (SHP), and the regularized M-estimators method is adopted to estimate the covariance matrix, then the eigenvalue decomposition (EVD) method is used to estimate the optimal phase in this process. Applying this DS-CTInSAR algorithm to 29-C band Sentinel-1 images with a 12 days revisit time from 2019/1/10 to 2019/12/24, we find that this technology greatly improves the density of measurement points, and compared with the NSBAS technology, the two results are consistent, exhibiting good correlations 0.91. The InSAR results show that the average annual deformation rate is −24.87~23.61mm/yr in the study area.

The bank of Nu River is controlled by several faults, and the geological environment is fragile. It is also affected by the natural factors of atmospheric precipitation. So there are a large number of potential landslide hidden points along Nu River. In this research we proposed an improved Time series InSAR (TSInSAR) technology for landslide investigation and identification from Anmuda to Kaxi along Nu River, China. The technique can identify high coherent scatterers and accurately obtain the surface deformation information. From the deformation rate map and Google earth optical image we identify five potential landslide hidden points along Nu River. We discover the continued acceleration phenomenon of hidden points that had been identified so that timely warnings of landslides with major potential safety hazards. This work is also helpful for the local government to carry out geological survey and disaster monitoring.

The Tibet plateau (TP), as the third pole, plays an important role for indicating the global climate change. This study evaluated the potential of SAR data on lake mapping on the southern TP region which is characterized by various complex water conditions. A robust object-oriented lake extraction approach using Sentinel-1 data is proposed. First, an automatic method of selecting regions that obeying bimodal distribution is used to obtain the optimal threshold to identify lakes. Second, Taguchi design experiments are used to obtain the optimal segmentation parameters of multiresolution segmentation and object-oriented classification is applied to extract the lakes. Finally, a postprocessing approach is applied to remove the false alarms due to shadows with auxiliary DEM data and object-based variance of intensity map. Experiments on the Sentinel-1 image demonstrate that the proposed method produces highly reliable lake extents in southern TP, and is potentially applicable to the lake mapping projects on the whole TP.

Jinsha River Valley area is located in the complex fault zone of southwest China. As the region has been strongly affected by landslide disaster in the past few years, it is very imperative to detect the regions with large deformation around this area. InSAR technique is widely used in obtaining the terrain displacement, especially for this inaccessible areas. Thus this work utilizes time series InSAR (TSInSAR) method to investigate the landslide risk for the Ruba-Lagang section of Jinsha River by using Sentinel-1 images. Several landslide-hidden areas are identified and the deformation time series for them are analyzed, which will be of great importance to the disaster prevention and local infrastructure construction.

Qinghai-Tibet Plateau (QTP) is often affected by climate change and anthropogenic activity. In this study, permafrost surface deformation is detected by time series InSAR method using high resolution TerraSAR-X images. In particularly, a sinusoidal function model is adopted in the parameter retrieving step for seasonal deformation extraction. The backscattering (σ°) of the main ground target types, such as alpine meadow, gross desert, mountainous slope, and railway, have been extracted. The evolutions of deformation and σ° of the main typical ground targets have been analyzed. Experimental result shows that σ° in alpine meadow areas increases about 10 dB from thawing season to frozen season. Experimental result shows that most area undergoes obvious displacement with the range from −15 mm/year to 15 mm/year.

A method to estimate absolute phase of the target, and phase compensation for the coherence matrix are introduced in this paper. The absolute phase of a target relates with the generator of target local shape, and with the the attenuation coefficients of vertical and horizontal polarimetric wave. The effectiveness of phase compensation is validated via Yamaguchi decomposition with phase compensation of Radarsat-2 full polarimetric data collected over Suzhou City, China. The estimation of absolute phase of target and the phase compensation are validated and benefit to correctly interpret the scattering mechanisms of targets, with decreasing the number of negative power pixels.

Permafrost is sensitive to climate change and anthropogenic activities. The interferometric synthetic aperture radar (InSAR) is a new technology allowing us to explore the interaction between the permafrost change and human infrastructures. In this paper, the deformation of the Qinghai-Tibet Railway (QTR) in Beiluhe of the Tibet Plateau (TP) between Jun. and Dec. 2014 is detected using the new mode TerraSAR-X interferometry. In the summer thawing season, the cross-profiles of the railway show the asymmetric “W” pattern settlement, with the local deformation maxima at the transition zones between the embankment slopes and the natural meadow. In contrast, the cross-profiles of the QTR have inverse heaving pattern in the freezing season. Differential settlement occurs along the QTR, probably due to its underlying permafrost conditions. Displacements of the embankments with different proactive cooling measures along the experimental embankment segment are derived, showing their cooling performance.

TomoSAR, as one of the hot technical topics these years, gives an advanced way to use the single orbit multiple baselines SAR images. Among the many TomoSAR methods, the sparse based spectrum estimator is the most popular one. In this paper, we make use of the truncation version of multiple snapshots of compressive sensing (MCS), called L1-SVD, for retrieving information in the urban area. Compared to other sparse-based methods, the multiple scheme excavates the potential valuable information to achieve the accurate spectrums and the truncation strategy improves the computational cost. To prove the compatibility of the L1-SVD of TomoSAR in urban area, an analysis of the snapshot is made and a validation using Radarsat-2 images of a stadium are processed. Finally, with the achieved sparsity of the stadium, the rough structure is retrieved.

In order to monitor the ground deformation due to coal mining, a new time-series InSAR technique combining PSs and DSs is presented in this paper. Firstly DSs are efficiently identified using classified information and statistical characteristics. Then a two-scale network is introduced into traditional PSI to deal with PSs and DSs. The proposed method is performed to investigate the subsidence of Huainan City, Anhui province (China) during the time of 2012-2013 using 14 scenes of Radarsat-2 images. Experimental results show that the proposed method can ease the estimation complexity and significantly increase the spatial density of measurement points, which can provide more detailed deformation information. The proposed method brings practical applications for non-urban area deformation monitoring.

Ship monitoring has a wide range of applications in marine activities and maritime management. Spaceborne synthetic aperture radar (SAR) technology has an advantage in ship detection over a vast region compared to other technologies. Recently, high-resolution SAR satellites have made ship recognition and classification possible from space. In July 2010, a ship recognition campaign was performed in the East China Sea. In situ investigation of ship types was carried out during the periods when the Italian COSMO-SkyMed satellites passed over the test site. Automatic Identification System (AIS) information was collected. Based on this information, a novel hierarchical ship classifier for COSMO-SkyMed SAR data was proposed. A total of 41 ship chips were cut from the SAR images for later classification. After preprocessing of the ship chips, geometric and backscattering characteristics of various ship types were analyzed. The ships were classified into bulk carriers, container ships, and oil tankers, with an accuracy of 93.3%, 80.0%, and 72.7%, respectively. Further investigation of the backscattering features with various illumination conditions is still in progress.

Ship wake detection can facilitate ship detection and ship motion parameters retrieval. In this paper, we propose a novel constant false alarm rate (CFAR) ship wake detection algorithm in SAR images based on length normalized scan method. As we all know, range component of ship's movement leads to azimuth offset in SAR images. The start point of wake should be located near the ship in the azimuth. According to this physical facts, length normalized scan is performed on line which may be a wake. By means of linear integral and length normalization technology, linear feature detection is transformed to point detection. Then the probability model in the scan domain is constructed to implement CFAR detection. Different resolution real SAR images are used to validate our proposed method. Experimental results show that this algorithm is effective for ship wake detection and can extract ship's velocity with good accuracy.

In this paper, we propose a polarimetric TomoSAR method with Wiener-SVD inversion and Gerschgorin Disks for different scattering mechanisms of each point. Seven images of RADARSAT-2 Fine Quad Mode have been used to validate the efficiency of the proposed method. The polarimetric analysis shows that more scattering points can be detected by the composition of different polarimetric bands, thus retrieving more information on the elevation within one resolution cell.

SAR co-registration is one of the crucial steps in the process of SAR image analysis, especially for multi-temporal SAR images analysis. Focusing on the repeat-pass space-borne SAR images, an auto-registration method for repeat-pass SAR complex image pair is proposed, which combines the Fourier transform and correlation-coefficient algorithm in a multi-scale scheme. Firstly, an initial offset is estimated in Fourier domain by using the 3rdlevel wavelet decomposition sub-image. Then taking these initial offset into account, match-search window pairs are randomly located over reference and sensed images for correlation analysis to get the identical points which are used for estimating the registration model in fine scale. In order to evaluate the proposed method, ALOS-PALSAR and RadarSAT-2 data in different area are selected as experiment data. And the experimental results show that the proposed technique allows the co-registration of the SAR images with the accuracy up to a fraction of a pixel automatically without a priori knowledge.

Synthetic Aperture Radar (SAR) is a significant tool to satisfy the growing demand of the maritime vessel traffic. ScanSAR, as an important role of SAR systems with very high imageries swath, is more suitable for the detection issue. In this paper, features of ships on Radarsat-2 ScanSAR imagery are characterized as “Bright-Dark” structure. According to the unique features, a new ship detector based on the Difference of Gauss (DoG) scale-space is proposed. To enhance the robustness, a threshold method simulated by the “dark spots” matrix is designed. In the threshold method, the average KL test is carried out with 6 different distributions on several Radarsat-2 ScanSAR Narrow imageries of sea which shows the Gamma distribution fits the sea clutter the best and is selected. Finally, the proposed detector is validated on a slice of Radarsat-2 ScanSAR imagery and comparisons with CFAR are made.

In this paper, vessel characterization is investigated using fully polarimetric Radarsat-2 SAR data. Polarimetric decompositions, including coherent decompositions, decomposition based on eigenvector analysis and model based decomposition, are applied to the quad-pol data to investigate vessel's scattering mechanism. Corresponding AIS and vessel photographs were acquired as ground truth data to facilitate analysis. Three types of vessels, such as bulk carrier, oil tanker and container ship, were investigated. Results show that the stable dominant scatters in a vessel and their scattering mechanism are expected as efficient features for vessel classification.

This paper introduces a new compact polarimetric (CP) three-component decomposition scheme, which can overcome the overestimation of volume scattering power in urban areas to a certain extent. For CP three-component decomposition, degree of polarization (DoP) is one of the key parameters, and directly influences the effectiveness of the decomposition method proposed in [1]. To overcome the overestimation of volume scattering component, a new method for estimating the DoP is introduced, and with the new DoP, volume scattering power decreases significantly. By considering the original decomposition method performed effectively in forest, lawn and ocean, the new DOP is only applied in urban areas. CP data in CTLR mode simulated from Radarsat-2 C-band fully polarimetric (FP) image is used to demonstrate the validity of the new decomposition scheme.

The high-resolution images, acquired by the new SAR sensors, provide opportunities to detect changes more accurately. The traditional pixel-based change detection method cannot adapt to the change detection of objects in different spatial scales, especially in build-up area, which has strong speckle noise. Recently, Object-based image analysis provides a feasible idea to solve this. In this paper, a SAR change detection method based on object-based image analysis is proposed. In proposed method, temporally sequential images are combined and segmented together to produce spatially corresponding image-objects. With different parameter settings, the segmentation maps of different scales are generated, which result in different difference maps. To utilize the information of different scales, a fusion method is employed to generate the final difference map, and a binary change detection map is produced on the basis of the difference map. The experiments show that object-based method is more efficient than the pixel-based method.

In this paper, we will propose a novel PolSAR change detection method applied to specific land cover type, i.e., from class ωito class ωj. Firstly, a new distance measure is derived to extract the difference map belonging to the specified change. Then, Kittler and Illingworth (KI) minimum error threshold segmentation method is applied to obtain the binary change mask. Two Radarsat-2 fully polarimetric images in Suzhou city, China, acquired on April 9, 2009 and June 15, 2010 separately, are used for our experiment. It is shown that the proposed change detection method will give a good performance to achieve the specified change areas of PoSAR images.

Thanks to the capability to operate in almost all weather conditions and during both day and night time, change detection (CD) based on SAR data is developed rapidly in recent years, especially with the successful operation of full polarization space-borne SAR system. Most of the CD methods based on Quad-pol SAR data are through the analysis of statistical characteristics of the polarimetric covariance matrix or coherency matrix. In this study, we proposed a new CD method by comparing the difference between the scatter components, obtained by polarimetric target decomposition, to extract the difference map and then an adaptive KI algorithm is carried out to find an appreciate threshold for segmenting to get the change. Two RADARSAT-2 quad polarimetric images acquired over the Suzhou City in China are analyzed for validation in the experiment and the effectiveness of the proposed method is presented in the result.

Persistent Sactterers Interferometry(PSI) technique is a series of advanced DInSAR methods which can reconstruct the time-series deformation of those coherent points in the direction of the satellite line-of-sight(LOS) with millimetric accuracy over a long temporal scale. It overcomes the problems caused by temporal and geometric decorrelations and the atmospheric dishomogeneities in conventional DInSAR by only considering the coherent pixels on temporal scale. Taking different polarimetric channels data into account, the same sense shows different coherence. In this paper we focuses on the benefits of using polarimetric data in PSI technique, as expected the different deformation results obtained from different polarizations can be mutually complementary to make the deformation estimation more robust, besides that, different polarizations are helpful to interpret the deformation.

This study presents a dynamic deformation retrieval method for differential synthetic aperture radar interferometry (DInSAR) processing. Detailed nonlinear displacement parameters are modeled and estimated for capturing the developing trends of ground movement both in time and space. In order to reduce the system disturbance errors such as atmospheres and platform deviations, the singular value decomposition technique is applied. 13 ascending ALOS PALSAR images from 2008/07/05 to 2010/08/26 are collected for the algorithm experiment. The studied area locates in Tianjin China and detailed deformation evaluation patterns have been obtained. The experimental results are validated via leveling data and prove the effectiveness of the algorithm.

In order to expand SAR application into vehicles monitoring, it is necessary to analysis and model the vehicle's characteristics under different SAR imaging conditions. In this paper, three typical trucks at different aspect angles were imaged by Radarsat-2 quad-polarimetric SAR image with different incidence angles in the field. RCS measurement and polarimetric decomposition were used to analysis the variation of vehicles phenomenon in SAR image. Some benefited conclusions are drawn based on analysis of the curve of RCS and polarization component which are varying with the aspect angle, incidence angle and structure.

This letter presents preliminary results of an attempt to monitor rice crop growth using RADARSAT-2 quad polarization synthetic aperture radar (SAR) data. Three RADARSAT-2 quad-polarization SAR images are collected from transplanting to rice crop harvesting. Ground truth data, such as rice height and biomass, are measured during RADARSAT-2 data acquisition in Hainan Province, South China. The correlation between backscattering coefficient and rice growth parameters is analyzed, and then, a rice field mapping method with quad polarization SAR image is developed. Experiments show that an HV or VH image backscattering coefficient exhibits the best correlation with rice age after transplantation. Furthermore, the HV or VH image is also more suitable for retrieving rice growth parameters, such as rice height and dry biomass, for FQ4 RADARSAT-2 SAR data. The ratio image of HH/VV possesses high separability required to distinguish rice crop from banana, forest, and river. Results indicate that RADARSAT-2 quad polarization SAR data presented enormous potential for monitoring rice crop growth.

First comparison experiments by different datasets were done to estimate the subsidence pattern of China Beijing-Tianjin Intercity Railway roadbed in Tianjin area in this paper. The multi-baseline differential synthetic aperture radar interferometry technique was used to give the subsidence monitoring. During the period of middle 2008 to middle 2009, the experiment results show that the roadbed is relative stable during the first year running of the Intercity Railway. And the comparison analyses show that both experiment results give the same subsidence rate pattern along the roadbed corridor, meanwhile the PALSAR results give better railway imaged but larger subsidence velocity standard deviation.

Radarsat-2 satellite offers general users the Quad-Pol SAR image service with a resolution of 8 meters, which provides valuable data source for the research of traffic vehicles monitoring on Quad-Pol SAR images. According to the vehicle target in such new SAR images, this paper put forward a target characteristic analysis method, which is a combination of target RCS measurement and polarization decomposition. Moreover, it choose the large trucks as an example, give out a conclusion of characteristic analysis of vehicle targets through the on-site synchronous experimental data obtained at different incident angle. Thus provides the research foundation for further exploration of realizing the traffic monitoring with space-born Quad-Pol SAR images.

On May 12, 2008, Wenchuan earthquake (Ms 8.0) occurred in Sichuan, Southwestern China. This catastrophe caused severe damage of constructions in urban and rural areas, and fundamental infrastructure, such as those facilities of factories, electrical power, telecommunication and transportation, etc. The earthquake also resulted in many geological phenomena, i.e. landslide, debris flow, landslide lakes, etc. which also triggered off damage and threat. The airborne campaigns were performed after the earthquake with high-resolution SAR system for rescue and relief effort and damage assessment. In this study, damage of various facilities was described and analyzed using airborne SAR images acquired during the campaigns. The results were verified with ground truth investigation. The results showed the role of SAR data in earthquake damage assessment.

In this paper, change detection with multi-polarization SAR imagery is focused. Based on the polarimetric statistic distribution, a polarimetric test statistic is applied to evaluate the equality of two areas in two pass polarimetric SAR images. And then, in order to find out the `real' changed area, a constant false alarms rate (Pfa) is given to determine a threshold for change detection. Moreover, a majority processing considering the context information of a given target is preferred to improving the final accuracy of the change detection. Finally, the proposed method was tested with the multitemporal Envisat-ASAR's alternative polarization SAR data. Experiments are performed to validate efficiency of the method presented in the paper.

In this paper, we apply small baseline DInSAR technique for the generation of surface deformation maps of the investigated area based on complex network. The technique estimates the linear deformation velocity in wide areas, not only in urban areas but also in suburban areas. The results presented in this study are obtained using 6 SAR data acquired by ENVISAT ASAR during 2004-2006. We compare the results with precise leveling data, which validate our results.

In order to meet the need of the development in interferometric SAR, especially the development of Permanent Scatterers InSAR(PS-InSAR) in these years, we develop a new software called CAESAR-XInSAR at the Remote-Sensing Satellite Ground Station, CAS. Both conventional InSAR and PS- InSAR are considered in this system. It supports the interferometric processing from SAR SLC data to end products of DEM or surface deformation map. It is organized in different modules and developed in Visual C++ 6.0 environment on PC under windows operating system. It is currently at the stage of the development from scientific platform to operational system. In this paper, the organization and the modules of this system are described and the results from CAESAR-XInSAR are also processed in the end.

Retrieval algorithm developed by Njoku et al. has been used by AMSR-E, but this algorithm has not yet been validated in desert area. In this paper, we use radiative transfer equation cooperated with bounded Levenberg-Marquardt method to retrieve land surface parameters such as surface temperature, dielectric constant, roughness and atmosphere moisture. By using climatic and meteorological data acquired by meteorology observation station in and around the desert, we then do a detailed validation. The result shows that the accuracy of retrieved temperature is within 2 K, the average dielectric constant of this area is 2.73 in winter. And the algorithm will underestimate surface temperature of desert in summer

High resolution airborne synthetic aperture radar (SAR) is useful for surveillance and remote sensing applications. The higher resolution leads to more speckle ad well as a need for more accurate pixel-level registration. Also, the curvature of the Earth needs not to be taken into account while registering two images because the swath of terrain being imaged in an airborne SAR is small. This article describes a new technique for registering multi-frequency airborne high-resolution SAR images. The image registration is set forth based on the fundamental principle of relaxation method, which uses image segment comprising of the relaxation registration results of point feature as global control. In order to guarantee the accuracy and reliability of image registration, line moment of edge feature is first used as match units to develop the traditional relaxation method. Airborne SAR images in Ku-band and X-band are used to test the new image registration method. Experimental results show that the proposed method outperforms standard image registration techniques in terms of reliability and rapidity in most cases. It also achieves better performance than wavelet pyramid method.