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.

High-resolution soil moisture (SM) products are of broad scientific interest and practical application value. The flexibility of estimating soil moisture from Sentinel-1 data has been widely recognized. However, the backscatter coefficient of synthetic aperture radar (SAR) data is heavily influenced by noise during radiation transmission as well as vegetation cover. This directly leads to the inability to accurately achieve high-resolution SM estimation using a single SAR data. In this paper, we proposed a deep learning model that fuses multisource data that estimates the SM in the depth of ~5 cm. The inputs to the model integrated remote sensing data and soil information data. The model was trained and validated on data from in-situ sensors of the international soil moisture network (ISMN). The proposed method achieved a coefficient of determination (R2) of 0.732 and a root mean square error (RMSE) of 0.069. In addition, the validation experiments in Anhui Province, China also demonstrated the effectiveness and robustness of the proposed method with a R2of 0.85.

The changes in urban built-up areas can reflect the process of urban expansion and have significant implications for evaluating sustainable urban development. The use of remote sensing to detect building areas is an important technology for land monitoring and urban management. Compared with optical images, SAR is less affected by interference and is an important data source for extracting building information in cloudy and rainy areas. Currently, high-precision automated extraction of built-up areas from SAR images remains a challenge. To address this issue, this paper proposes a semantic segmentation framework for building area extraction based on HRNet. Using multiple temporal SAR images acquired from Hainan Province as an example, the paper analyzes changes in the built-up areas of the province by combining the results of built-up area extraction over several years.

Atmospheric phase screen (APS) is the dominant error source of InSAR. The spatial-temporal variations of APS in interferograms can lead to incorrect interpretation of phase and inaccurate extraction of surface deformation. In recent years, deep learning denoising models have been applied to study the features of APS in InSAR interferograms and extract deformation information in existing researches. However, the characteristics of APS are diverse, and it is difficult to extract this knowledge using a deep learning model based on limited interferograms that contain APS. Moreover, existing methods mainly use synthetic data to train the model and remove APS. In order to provide sufficient APS data for training deep learning networks, this paper proposes a new method for generating atmospheric phase samples. This method is based on the auxiliary classifier generative adversarial network (ACGAN) to generate more APS samples from available interferograms, fully learning the atmospheric delay errors related to terrain, atmospheric turbulence, and heavy rainfall, and generating atmospheric phase screen interferogram samples with multiple features. The technique is applied to 235 interferograms collected over Hangzhou on ascending orbit number 108 between January 12, 2020, and December 3, 2022, to generate different types of atmospheric sample screen interferogram samples. The network has shown great potential for the removal of atmospheric phase in InSAR research.

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.

Reliable and accurate rice mapping using synthetic aperture radar (SAR) in cloudy and rainy areas is essential for achieving the United Nations Sustainable Development Goal 2 of 2030. An interpretable deep learning SAR rice area mapping method is proposed in this letter to suppress the interference of wetlands and other land covers to multi-temporal SAR rice area mapping and improve the accuracy and confidence of the "black box" deep learning model results. Combining the temporal backscattering coefficients and interferometric coherence, three interpretable temporal features are extracted to effectively distinguish rice. Then, the explainable feature-aware network (XFANet), which can provide the learned importance weights of the normalization methods as self-interpretation, is constructed, and the pixel-wise gradient-weighted class activation mapping (PGCAM) post-hoc interpretation method is introduced to interpret the feature variation within the model. The experimental results in the Kampng Chhang and Kampng Chham provinces of Cambodia show that the proposed three interpretable features well suppressed the wetland disturbance to rice. With high interpretability, the overall accuracy of XFANet reaches 93.43%.

Earthquakes are catastrophic in terms of damage to buildings. Synthetic aperture radar (SAR) has emerged as an effective tool to respond to seismic hazards. However, pre- and post-event high-resolution data are not always available for the affected areas, and the complex geometric properties of buildings pose a challenge to building damage detection. Therefore, this letter proposes the Dual-Domain Transformer (DDFormer) semantic segmentation model for damaged buildings detection using a single post-earthquake high-resolution SAR image. The difference between intact and collapsed building features is enhanced by adaptive frequency and spatial modules. Taking the 2023 Turkey earthquake as an example, the experiments are conducted on two high-resolution co-polarized SAR data (Capella and GF-3). The DDFormer achieves optimal detection accuracy with mean IOU (mIOU) and F-Score of 81.81% and 90%, respectively. In addition, our results are in high consistent with the Turkey Earthquake Report published by Microsoft with a correlation coefficient of 0.626. The above experiments demonstrate the robustness and effectiveness of DDFormer.

Synthetic aperture radar (SAR) image despeckling is a challenging task as speckle noise is spatially correlated and signal-dependent, and appears as a grainy texture superimposed on images. Although traditional low-rank SAR image despeckling methods have shown promising performance, they have the problem of producing over-smoothed images with blurred edges due to their low-rank characteristics. In this paper, we propose a novel edge preserved SAR despeckling method named EP-LRSID, which can keep rich edge details while reducing speckle noise. Specifically, EP-LRSID takes a fresh look at the low-rank model, i.e., we can obtain structural edge information from residuals which is viewed as noise and simply disregarded by the traditional low-rank methods. To obtain discriminative edge information from residuals, the edge subspace is obtained in a manifold framework by using a dynamic affinity graph regularization. Moreover, a new hierarchical prior knowledge regulation is designed to make different kinds of pixels processed hierarchically, especially the strong scattering points in SAR images. By introducing this prior knowledge, our low-rank model can obtain more confidential low-rank parts and edge parts, thus structural information including edges can be better preserved in this way. Extensive experiments on several real and synthetic datasets demonstrate that EP-LRSID can achieve the highest despeckling performance with edge preservation than other state-of-the-art despeckling algorithms.

With the development of urbanisation in China, the urban areas are expanding rapidly, but there is a huge regional disparity between the east, central and western regions. The urban development in the western region lags far behind that in the eastern and central regions. In the western region of China, due to the large number of mountains and SAR backscatter mechanism, there are a lot of overlays in the image, resulting in high false alarms in built-up areas segmentation. In order to solve the problem, this paper proposed a new built-up area extraction model based on the Transformer. Different from the segmentation method based on convolutional neural network, the self-attention mechanism of the Transformer was introduced to effectively capture the image context information and reduce the impact of mountain overlays on the extraction of built-up areas. The multi-layer Transformer encoder and the multilayer perceptron (MLP) decoder were used to fuse feature maps of different scales for the sake of enhancing the ability to extract architectural features. With the purpose of improving the generalization ability, various data augmentation methods were used during training, such as random noise, random blur, and random distortion. In this paper, about 32000 samples, including some mountainous areas and around China, were used for training, which are from 27 scenes of GF-3 10m SAR images covering different areas in China. The method proposed in this paper reached a mIoU of 0.8130 and a Kappa coefficient of 0.9423, which significantly reduced false alarms in mountainous areas. Taking the study area of Lanzhou City, Gansu Province of China as an example, the result is basically consistent with the classification map of World Cover. It shows that the proposed method has a good ability to extract the distribution information of built-up areas.

In recent years, deep learning has achieved great accuracy in SAR image ship detection tasks. However, for large-scene SAR images, the sliding window method will generate a large number of slices sent to the network, resulting in a waste of GPU resources. To solve this problem, we proposed a ship detection algorithm based on CFAR, which significantly reduces the number of slices and false alarms on land. Firstly, the sea regions to be detected are obtained under the prior guidance of Global Self-consistent Hierarchical High-resolution Shorelines(GSHHS), and remove major land areas in the SAR image. Secondly, the ship candidate region generation algorithm based on block G0-CFAR is implemented to obtain the candidate slices. Finally, the Yolov5 model is used to detect ships from the candidate slices. The validation experiment of sentinel-1 and Gaofen-3 large-scene SAR images shows that the proposed method can detect the ships in large-scene SAR images with 88% detection accuracy within 97s, and the number of image slices fed into GPU is greatly reduced. It has certain practical significance for ship detection in large-scene SAR images.

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.

Image generation by the conditional generative adversarial network (CGAN) can provide rich samples for supervised classification tasks. However, the unstable gradient updating problem makes it difficult to be used for image generation, especially for the high-resolution SAR image. In this letter, an improved residual condition generation network is proposed to enhance the quality of generated images and classification accuracy for hard negative examples. First, a residual convolutional-based block is built to clear the detail texture of different types of targets. Then, the gradient penalty and Wasserstein loss are used as discriminators to improve the similarity between real samples and the intra diversity of a generated images. We test the proposed method on generation and classification of three kinds of commercial ships using C-band 3-m Gaofen-3 (GF-3) SAR images. Experimental results show that our method can generate high-quality ship samples and achieve good classification accuracy.

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.

In this paper, an automatic extraction method of multi-type built-up areas in SAR images is proposed. In order to adapt to the architectural differences in different regions, we improve the residual U -Net, one is to introduce multi -scale pyramid structure into the structure, the other is to use the diss loss function. The proposed method was verified using GF-3 SAR data in four regions of China and compared with FCN and PSPNET. Finally, the accuracy evaluation results show that the overall accuracy of the extraction results is greater than 86%, indicating that the method has a certain application potential.

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.

In this paper, a ship classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the classification results on deep learning-based methods. A representative SAR ship dataset containing three types of ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo ship as the example, the experimental results show that when using data with different range of incident angles for training, the classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR ship classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify ships in SAR images, and may suggest satellite parameters based on the classification results.

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.

Deep learning has been successfully applied to the ordinary image super-resolution (SR). However, since the synthetic aperture radar (SAR) images are often disturbed by multiplicative noise known as speckle and more blurry than ordinary images, there are few deep learning methods for the SAR image SR. In this paper, a deep generative adversarial network (DGAN) is proposed to reconstruct the pseudo high-resolution (HR) SAR images. First, a generator network is constructed to remove the noise of low-resolution SAR image and generate HR SAR image. Second, a discriminator network is used to differentiate between the pseudo super-resolution images and the realistic HR images. The adversarial objective function is introduced to make the pseudo HR SAR images closer to real SAR images. The experimental results show that our method can maintain the SAR image content with high-level noise suppression. The performance evaluation based on peak signal-to-noise-ratio and structural similarity index shows the superiority of the proposed method to the conventional CNN baselines.

Urban building change detection is one of the most important parts of remote sensing applications. Researching change detection method based on deep learning is an effective solution to monitor the urban expansion and recognize the specific change classes. In this paper, we propose a novel urban building change detection method based on the revised residual Unet with Sentinel-1 SAR intensity images. Firstly, we present a new difference image by combing both the original intensity image and the enhanced log-ratio difference image using a non-linear function. Then, the combined difference image is sent to a revised residual Unet network to detect the building changes. By the proposed combined difference image and the revised network, our method is able to focus on the building's change while ignoring other land type changes in a large area. A pair of real bitemporal SAR images is used to test the proposed approach and the obtained experimental results confirm its effectiveness.

High resolution synthetic aperture radar (SAR) data have important research significance and application prospects in crop fine classification. In order to make full use of high resolution SAR data, this paper introduced the deep learning semantics segmentation methods, which have already achieved tremendous success in the field of computer vision, into the corn fine classification using SAR images. Therefore, a corn fine classification method, based on U-Net network for high resolution SAR data, was proposed in this paper. Experiments were carried out using one scene high resolution GF-3 SAR data covering Fuyu City, Jilin Province, China. The experimental result shows that the proposed crop fine classification method based on deep learning semantics segmentation method can achieve a satisfactory classification result.

Building damage assessment with remote sensing images plays an important role in providing information for disaster rescue and reconstruction. Recently, deep convolutional networks show good ability for some remote sensing applications. However, it is difficult to obtain a large number of labeled samples for training in some cases. With regard to this problem, a transfer learning method based on deep neural networks is adopted to discriminate collapsed building from intact building in remote sensing images in this paper. Samples are obtained from high resolution remote sensing images and split into training and validation datasets. Then a collapsed building discrimination model is built based on very deep convolution networks (pretrained VGGNet via ImageNet). With the new trained VGG model, test dataset including samples of collapsed buildings and intact buildings are used to classify the two types of buildings. The preliminary experiment results show that the new trained model performed well for collapsed building discrimination.

With the global climate changing and increasing of anthropogenic activity, the dynamics permafrost environment of Qinghai-Tibet Plateau (QTP) is becoming fragile. As one of the most important infrastructures in the QTP, the Qinghai-Tibet Power Transmission Line (QTPTL) has been constructed since 2011. In some section, the stability of QTPTL has been damaged due to the harsh climate and the effect of freezing and thawing of permafrost. In this paper, structural health and stability of QTPTL are evaluated using time-series InSAR with TerraSAR X-band data. The structure feature of the transmission line tower in high resolution SAR image are analyzed. Then the deformation velocity, height and thermal dilation of the QTPTL is retrieved.

Deep convolution neural networks (DCNN) have been successfully introduced in the field of Polarimetric SAR image classification. However, the commonly used DCNN will classify each pixel in the image and neglect the fact that neighboring pixels may have similar intensity. Besides, the fixed size input in DCNN cannot be well adopted in remote sensing image which includes a great deal of different-scale information. Thus, superpixel segmentation (SS) and the input pyramid are introduced in this paper to improve the performance of DCNN. The former will guide the DCNN to classify superpixel instead of single pixel and the latter will include different-scale information around the pixel. Experiments carried out on two scenes of ALOS-2 PALSAR-2 POLSAR images demonstrate that the introduced technic can help DCNN achieve good accuracy and smooth boundary adherence with highly efficiency.

With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.

In this paper, the Sentinel-1 ScanSAR IR GRD products are used for sea ice mapping using convolutional neural networks (CNN). The sea and ice are classified as 2 types and 4 types respectively according to their SAR image textures. They are smooth sea, rough sea, granular ice, massive ice, smooth ice and striped ice. The Sentinel-1 SAR images are firstly pre-processed using ESA SNAP software. Then the classes are interpreted manually for chip preparation and annotations. Chips with 3 spatial scales (32x32,64x64,128x128) are used for training input of the CNN. The trained CNN is then used for generation of sea ice map from the ScanSAR image. The results are promising. Further work is still going on.

As one of the most essential economic and industrial crops globally, corn holds a very important position in China's agricultural industry. Corn mapping is one of the most concerned fields in agricultural surveillance. However, compared with the utilization of backscattering coefficients, the polarimetric information was not fully discussed in previous corn mapping researches. In this paper, we use the coherency matrix of mid to late term multi-temporal fully polarimetric synthetic aperture radar (FP SAR) data to discriminate corn cultivation areas. The tensor representation is adopted for PolSAR analysis, with the help of multilinear principal component analysis (MPCA) to reduce feature dimensions. The importance of polarimetric information is discussed. This paper illustrates that good corn discrimination could be achieved with only mid to late term FP SAR data.

The detection and extraction of buildings using high resolution synthetic aperture radar (SAR) images has been the topic of recent discussions. In this paper, a framework for building extraction and classification is proposed. Buildings are classified into three kinds: commercial architecture, residential building and Industrial building, by the fusion of structure features (point-like, linear and regional features). All kinds of structure features are detected orderly in the framework of multi-scales pyramid. The delineation of urban footprint is firstly determined on the highest scale level, and then the types of buildings are classified on the lowest scale level based on the local features including persistent points, corner lines, and dark shadows, which are detected in the pre-determined region of urban footprint. The experimental results on our TerraSAR-X Staring Spotlight(ST) data set confirm that our scheme performs well.

Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep architecture can obtain robust high-level features directly from raw data. A drawback of most unsupervised representation learning methods in SAR ATR is that they only deal with amplitude images. In addition, many methods utlize a single layer architecture to extract pixel-level/mid-level features which are probably sensitive to condition variation. In this paper, a feature extraction method based on modified stacked convolutional denoising auto-encoder (MSCDAE) for complex SAR images is proposed, where convolutional kernels of MSCDAE are learned by 1-D modified denoising auto-encoders. By stacking the convolutional layers and pooling layers, high-level representation of objects are learned. The features are subsequently sent to a trained SVM for object classification. Experimental results demonstrate that the proposed method can provide a significant improvement in the ATR performance.

Ship classification with spaceborne high resolution synthetic aperture radar (SAR) has wide applications in maritime traffic monitoring, fishing law-enforcement operation, marine security, etc. Deep learning, which has the ability of learning features itself, is successfully used in computer vision and artificial intelligence, and introduced into remote sensing field in recent years. In this study, the Italian COSMO-SkyMed SAR images acquired on Jul. 12-15, 2010 were used for ship classification with convolution neural networks in the Google's TensorFlow environment. The results show that cargo ships could be discriminated from non-cargo ships. Due to variations of radar illumination directions and ship poses, more data are necessary for sub-category classification.

This paper displays a case study which accomplishes crop classification of simulated compact polarimetric (CP) SAR and fully polarimetric (FP) SAR images with Random Forest. Since the potential of CP SAR in classification has been illustrated by various researches, we intend to find out which of the polarimetric features are more superior in crop classification, through the importance rank of Random Forest classifier. Experiments are carried out based on an L-band AIRSAR FP SAR image and an ALOS-2/PALSAR-2 SM-2 FP image. Comparison analysis of feature importance between CP and FP demonstrates the intrinsic connotations of selected polarimetric characteristics, which provide guiding information for future investigation of CP SAR classification.

Change detection is a hot issue and is of great significance in remote sensing. The logarithm operation is a valid way to reduce the influence of multiplicative noise in the Synthetic Aperture Radar (SAR) image. However, changed areas with high gray level values will be weakened due to the nature of the logarithmic function. In this paper, a SAR image change detection framework based on visual attention is proposed. In the proposed method, the SAR image change detection is finished with an extreme method with darkness and brightness on the vision. The main process can be divided into two parts according to dark and bright image patches. The dark changed areas are validly detected via a weighted logarithmic function, which has strong noise immunity. The weak bright changes are taken as noise. The saliency extraction is applied on the initial SAR image patches to enhance the bright changed areas whereas others present murky background. Then bright changed areas can be validly detected using kernel fuzzy c-means (KFCM), in which the cross-time similarities function between image patches is used. Finally, two change maps can be added to obtain final result. The real SAR image pairs of Suzhou area are used to verify proposed change detection method. The experimental results demonstrate the effectiveness of the proposed method.

To explore the capability of quad polarimetric SAR images from a new pursuit monostatic mode of TerraSAR-X and TanDEM-X, a novel process chain for ship detection and velocity estimation is proposed in this paper. Compared with the classic processing chain used for single SAR image, more efficient techniques are integrated into this novel chain, which take advantage of the new image mode and its excellent imaging quality in term of the radiometric and geometric accuracies. Land masking based on coherence optimization and ship velocity estimation depended on the difference of positions from individual images are proposed as significant improvements in the new process chain. Their efficiency is validated with the experimental SAR images acquired over East China Sea. The experimental results show that the land regions and small islands are entirely removed and the estimation results of ship velocity is close to the ground truth from automatic identification system (AIS) data.

Ship detection using synthetic aperture radar (SAR) is an important application in maritime transportation monitoring. However, azimuth ambiguities are inevitably caused by the undersampling of the echo signals, which can be easily mistaken as ship targets in the detection result. To solve the problem mentioned above, a fast object-oriented method for azimuth ambiguities removal based on quantitative analysis is proposed. The main idea is to manage to find the ambiguities' accurate locations in the image based on the imaging parameters, so that these “ghosts” can be removed easily. It is shown that the displacement distances between the ship and its corresponding azimuth ambiguities can be calculated approximately. In case of removal of some small ships with weak backscattering power, the energy decay of the ship is also taken into account. After the ambiguities' location were fixed, a searching procedure was executed to remove them. The experimental results show the proposed method is able to remove azimuth ambiguities efficiently.

In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information from training samples. The combination of various polarimetric features are considered as original input feature sets. And then SLLE technique is implemented to map the original high dimensional space into a low dimensional manifold for subsequent classification. The experiments on fully polarimetric data show that SLLE can acquire more accurate classification results in comparison to many traditional DR techniques.

Coastal discharge plume is one of the most important pollution hazards for the heavily polluted estuaries in Shenzhen, China. Due to their dynamic and episodic nature, these pollutions are difficult to sample adequately using traditional in situ measurement methods. This paper introduces a plume extraction method using fully polarimetric synthetic aperture radar (SAR) image. TerraSAR data acquired over Dongbao River in Shenzhen, China, is used in our experiment. The Cloude-Pottier decomposition method is applied on the data to obtain scattering components. And the results show that the wastewater plumes could be successfully observed and extracted by the proposed method.

This letter depicts a ship detection scheme for synthetic aperture radar images, utilizing a segmentation based global iterative censoring algorithm. In the proposed scheme, the fuzzy local information c-means clustering (RFLICM) algorithm is adopted to partition the inhomogeneous SAR image into numerous homogeneous sub-regions, thereby eliminating the performance degradation caused by SAR image inhomogeneity. Subsequently, successively applying the GIC algorithm base on a parametric clutter model database to the sub-regions, the optimal clutter models and the initial outlier map of the sub-regions are generated. A sliding window CFAR detector based on the selected clutter models and the initial outlier map is utilized to detect ships in the SAR image. In our experiment, we tested the proposed method on spaceborne SAR data, and its effectiveness was successfully demonstrated.

In this letter, an improved kernel density estimation (KDE) constant false alarm rate (CFAR) method is proposed for ship detection in single polarization synthetic aperture radar (SAR) images. The proposed method consists of a target enhancement filter, an adaptive KDE bandwidth estimation method and an improved KDE-CFAR. The gravity-based target enhancement filter is utilized to remove the inhomogeneity in SAR images, and thereby meet the requirement of the KDE bandwidth estimation method. The proposed method provides an automatic training sample selection scheme, avoiding the manual intervention in conventional method. In addition, the KDE-CFAR is improved, employing the exponential function as the kernel since it provides an analytical solution for the CFAR criterion, which is unavailable for the Gaussian kernel. Experimental results with six spaceborne SAR images demonstrated that the proposed method is effective and efficient for ship detection application.

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture of permafrost is the important factor influencing the energy flux, ecosystem and hydrologic process. Synthetic aperture radar (SAR) provides us a powerful tool to monitor the soil moisture. In this study, 19 scenes of German TerraSAR-X data are used to retrieve the soil moisture in Beiluhe, Northern Tibet. The field campaign was performed on Aug. 12, 2015 and Mar. 8-13, 2016 during the TerraSAR-X overflight to acquire in situ soil parameters, together with the temperature and precipitation data from the weather station. Two approaches are proposed, one is based on time series observations, and the other is based on AIEM. Promising results are obtained.

Technology for marine vessel monitoring and analysis combining Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) is presented in this paper. Detection results in SAR image are validated and analyzed with corresponding AIS information. In order to evaluate the performance of the techniques, nine ERS-2 SAR images covering the year 2007 and corresponding AIS polls are used. The results were analyzed and discussed combining SAR and AIS reports. Based on the analysis, conclusions can be made that SAR and AIS have relative advantages and disadvantages respectively, using both systems together is a good way for marine traffic monitoring.

Land cover change detection has long been a hot field in polarimetric synthetic aperture radar (SAR) applications. In certain cases, we care not only the changed areas but also from which type to another. This paper presents a supervised urban land cover change types identification method using a series of polarimetric descriptors from SAR observables and polarimetric decomposition. The normalized difference ratio (NDR) operators are generated firstly using the polarimetric descriptors from SAR images acquired in different dates; these operators are then trained according to the selected training sets and used to identify the remaining samples. A modified superpixel segmentation method for polarimetric interferometric SAR (Pol-InSAR) datasets is introduced to improve the accuracy of the experimental result. Radarsat-2 fully polarimetric SAR (PolSAR) images acquired over Suzhou city, China on 9 April 2009 and 15 June 2010 are used in our experiments, the identification accuracies for all changed land cover types are over 80%, which demonstrates the effectiveness and usefulness of the proposed method.

Plant biomass is an important parameter for crop management and yield estimation. The potential of compact polarimetric (CP) synthetic aperture radar (SAR) data in estimating biomass of oilseed rape crop (Brassica napus L.) is investigated in this study. Five CP SAR imagery was simulated using five fully polarimetric Radarsat-2 data, and the dynamic evolution of polarimetric features, relying on different polarimetric decomposition methods (m-χ, m-δ, and Freeman-Durden), with the crop growth, was compared. It was found that the Dbl indicator, by the m-χ decomposition method, can reflect well the dynamic growth of canola. Therefore, a method of monitoring fresh and dry biomass of canola was put forward. The result showed that the root mean square error (RMSE) was 56.5g/m2, 448.2g/m2, and the relative error (RE) was 23.9%, 25.0% for fresh and dry biomass, respectively. In addition, the precision of the model will be affected when the crop becomes mature since its vegetation water content declines. The results were also compared with those of the fully polarization SAR. It revealed that the performance of CP SAR on rapeseed monitoring can achieve the level of fully polarization SAR, considering the advantages of CP SAR, such as wider coverage and less data volume etc. It revealed that the polarization information was necessary in quantitatively monitoring of broad leaf crops, such as rapeseed, and CP SAR has a great potential in crop monitoring.

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.

Under the condition that the accuracy of the air-borne platform is unknown, an affine transformation model made up of unknown parameters such as rotation, scale and translation is proposed for Synthetic Aperture Radar (SAR) image geometric correction. These parameters in this model have real physical meanings compared to that in the traditional polynomial models. Based on the characteristics of geometric distortions in SAR images, four unknown parameters, i.e., one rotation angle, two scale and two translation parameters, are selected for geometric correction. Different imaging look directions modes including left and right are all considered in this transformation. One real air-borne SAR image with 0.2m resolution was used to validate the availability of this method. In addition, Ground Control Points (GCP) were surveyed by the technique of Real Time Kinematic (RTK) to resolve the unknown parameters and evaluate the image geometric correction results.

In 2013, the TerraSAR-X (TSX) mission was extended by implementing two new modes, including Staring SpotLight (ST). The azimuth resolution of this mode is significantly increased to approximately 0.24 m by widening the azimuth beam steering angle range. In this letter, five types of damaged buildings due to earthquake are analyzed using TSX images in the new ST mode. In particular, the characteristics of the individual damaged buildings are investigated using the new very high-resolution images. The old Beichuan County is selected as the research area, where most of the buildings damaged by the earthquake on May 12, 2008 have been preserved. Moreover, buildings with different types of damage can be found in the study area. Features such as backscattering and texture measurements of the damaged buildings in descending and ascending post-earthquake HH and VV polarization synthetic aperture radar (SAR) images are analyzed. Visual interpretation, statistical comparison, and classification experiments are performed for the damaged building analysis. Based on the analysis, the TSX ST mode images show potential for building damage detection and for discrimination of basic damage classes. In addition, the classification results show that the gray-level co-occurrence matrix (GLCM) second moment, the variance of backscattering, and the GLCM homogeneity are the three best features for the discrimination of damage type.

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.

High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.

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.

Recently, there has been an increasing interest in compact polarimetry (CP), which helps to reduce the complexity, cost, mass, and data rate of synthetic aperture radar (SAR) systems while attempting to maintain many capabilities of a fully polarimetric system. In this letter, we provide a comparison of the capabilities of three conventional CP modes to distinguish different physical scattering mechanisms (PSMs) through H-α decomposition. A compact H-α space for dual circular polarimetric mode is proposed on the basis of the distribution centers and densities of different PSMs. Both airborne and spaceborne SAR data are used to validate our method. The experimental results show that the decomposition accuracy of each PSM is improved compared with the results of previous publication, the kappa coefficient increases by nearly 0.1, and the overall accuracy is above 81%.

In this paper, we present a novel unsupervised change detection scheme for multilook polarimetric synthetic aperture radar (PolSAR) images using heterogeneous clutter models. First, a multilook product model is introduced to describe the heterogeneous clutter for multilook PolSAR data, and a corresponding covariance matrix estimation method is derived. Based on this model, a new similarity measure is proposed to quantify the degree of evolution between the statistical characteristics of multitemporal fully PolSAR images. Compared with the classical similarity measure of Wishart distribution, both the structure of the covariance matrix and the power information of PolSAR clutter are considered in the proposed similarity measure. A Kittler and Illingworth (K&I) minimum-error threshold segmentation method is applied to extract the changed areas. Both the simulated PolSAR data set and two three-look Radarsat-2 fully polarimetric images of Suzhou, China, acquired on April 9, 2009 and June 15, 2010, are used for our experiment. The results demonstrate that the proposed change detection method can give a much higher detection rate and a lower false alarm rate than the method using the Wishart similarity measure.

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.

This paper proposes a new detector for interested object detection in high resolution SAR image. To obtain higher performance, four parameters are required to describe features of the interested object. Specifically, the parameters of segmentation scale and average expected object size are used for SAR image segmentation to get patches, which are small image parts to capture fine scale information such as gray variance and shape. In this step, adjacent map is adopted to recode the information and the relationship of all the patches; The third parameter is false alarm rate, which is used to retrieve the intensity threshold for finding out all the potential targets in SAR image; The parameter of object packing density is finally used to reduce the false alarm, which is calculated according to their occurrence frequency. To evaluate the proposed detection methodology, air-born SAR image with one-meter resolution is tested. The scene context of this image is complex, since it includes building, road, vegetation and trucks. If the trucks are taken as the interested objects, this methodology can detect all of them with lower false alarm rate.

It is necessary to calibrate polarimetric synthetic aperture radar (PolSAR) data in order to use the data for science applications. In this letter, we propose an improved algorithm to estimate the calibration parameters based on the Ainsworth algorithm and the Quegan algorithm. Since there is no approximation in the process of the parameter estimation, this algorithm can accurately solve all parameters, even if the crosstalk is high. To verify the effect of the proposed calibration algorithm, this letter analyzes the accuracy of the proposed algorithm with the simulated PolSAR data and Chinese airborne X-band PolSAR data. The results confirm that the proposed algorithm can provide a more stable and accurate solution to the crosstalk parameters.

Ship classification in high-resolution synthetic aperture radar (SAR) satellite images is a hotspot and a continuing problem in SAR applications. The scattering components of ships are the strong scatter of objects in SAR images, and these can represent the superstructure of different ship types. Based on analyses of different scattering components of bulk carriers, oil tankers, and container ships, we propose a new classification method for these three ship types in COSMO-SkyMed SAR images. First, morphological preprocessing is applied to suppress sidelobes. Second, based on Hough transform (HT), the orientation of the principal axis is extracted, and the modified minimum enclosing rectangle (MER) of the ship is obtained and rotated along the principal axis. Finally, the ship type is decided according to the width ratio of MER between the HT line, the ratio of ship and nonship points on the principal axis, and the scattering density. The results show that this method has good performance in ship classification, with an overall accuracy of over 80%.

Ship detection is an important application of global monitoring of ocean environment and maritime traffic. Synthetic aperture radar (SAR) systems are active sensors offering unique good spatial resolution regardless of weather or other conditions. It has been widely used for ship detection. An improved ship detection for high resolution SAR images based on the ship feature confidence is proposed in this paper. The features include kernel density estimation, length-width ratio and the number of target pixels. Targets with high feature confidence will be interpreted as ships. The COSMO-SkyMed SAR image is adopted for investigating the proposed algorithm. Experiment results illustrate that the method can achieve good performances.

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.

Ship detection and monitoring techniques are important applications in protection of marine resources. In this paper, a ship detection method is proposed based on the Freeman three component decomposition and OS-CFAR detector. The detector has been applied on RADARSAT-2 QUAD Mode image to demonstrate the efficiency of the method. Compared with polarimetric white filter, SPAN image, HH-CFAR and HV-CFAR methods, the proposed method can detect ships in ocean clutter correctly and control false alarms well. Moreover, this method does not depend on the size of targets and therefore offers more adaptability for ship detection.

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.

Polarimetric information can be used to characterize the target and benefit for ship classification in SAR image. In this paper, three types of features from fine quad-polarization Radarsat-2 SAR image, such as target to clutter ratio, distribution of scatter point, coherent decomposition component, are analyzed of three types of ships such as bulk carrier, container ship and oil tanker. Preliminary results show that target to clutter ratio is not suitable to discriminate the three types of ships. HH polarization is optimal for ship detection to HV,VH and VV polarization under the condition of the incidence angle of about 48 degrees. Distribution of strong backscattering can reflect the geometry structure of ship in SAR image, and it can be a good feature for ship classification. Furthermore, the feature will be more reliable in higher resolution SAR image. Polarimetric coherent decomposition benefits for understanding scatter mechanism of scatters of different ship. However it has some limitations for ship type recognition. More features and more ship chips should be investigated for the further conclusion.

It's worth to investigate that the false alarm is reduced with polarimetic information while keeping the detection ratio when the fully polarimetic SAR images used for ship detection. An improved S-NMF (Sparseness-Nonnegative Matrix Factorization) for ship detection according to the nonnegative and sparseness characteristics of eigenvalues is proposed in this paper. The first two eigenvalues are used for NMF decomposition since they take most of the energy of the targets, and then we combine the advantage of the third eigenvalue which could get rid of the false alarms for ship detection with the OS-CFAR method. The proposed method is validated using Radarsat-2 fully polarimetric images with the corresponding AIS data. Results show that it cannot only detect most of the ships, but also reduce the false alarms effectively, and has good ability when the detection conditions change.

An improved four-component model-based target decomposition scheme for polarimetric synthetic aperture radar data is proposed in this letter. The reason for the emergence of the negative powers in the Yamaguchi decomposition has been analyzed, and three corresponding additional steps are added in the proposed scheme. First, the orientation angle compensation is applied to the coherency matrix. Second, the coherency matrix with the maximum entropy, i.e., the identity matrix is used as the volume scattering model instead of the traditional ones. Third, corresponding power constraints are appended to the scheme. Moreover, the densely vegetated areas and the residual areas are processed separately via the H/α/A classification in the proposed scheme. Finally, the polarimetric-scattering-characteristic-preserving classification is utilized to verify the improvements of the proposed scheme. To demonstrate the effectiveness of the decomposition, an Advanced Land Observing Satellite Phased-Array-type L-band Synthetic Aperture Radar polarimetric image acquired over Beijing, China, is analyzed, and the results are presented in this letter. With negative powers eliminated by the proposed scheme, improvements can be observed in the experimental results, particularly for the urban areas.

A four-component model-based decomposition for polarimetric synthetic aperture radar (SAR) images is proposed to deal with the ground objects with orientation angles around 45$^{\circ}$. In the previous decompositions, these special targets are mixed with the vegetated areas. With the deficiency of the previous decompositions analyzed, the ambiguity between two scattering mechanisms is clarified. A rotated Fresnel dihedral reflection model is introduced in the proposed algorithm, to model the scattering characteristics of these special targets. The nonnegative eigenvalue decomposition is applied to the remainder coherency matrix to prevent negative powers of the decomposed scattering mechanisms. Another advantage of the proposed decomposition is that it makes use of all the information provided by the coherency matrix, which remains unachieved in the previous model-based decompositions. Experimental Synthetic Aperture Radar (E-SAR) L-band polarimetric SAR data acquired over Oberpfaffenhofen, Germany, are analyzed in this letter. Experimental results indicate that the special ground objects have acquired correct scattering mechanisms, which verifies the effectiveness of the proposed method.

In this letter, we investigate the polarization coherence tomography technique and propose a new function expansion to reconstruct the vertical profile function. Instead of generating profile in Fourier-Legendre series, we deduce orthogonal functions on [-1, 1] by weight ofz2, which can increase the highest polynomial order and decrease the condition number of the inversion matrix, indicating that the inversion in the new expansion is more stable and less susceptible to noise. Finally, we apply the technique to simulated dual-baseline data and Chinese X-band single-baseline polarimetric synthetic aperture radar interferometry data to demonstrate its validity and robustness.

The coherence optimization methods of compact polarimetric SAR interferometry (PolInSAR) are analyzed in this paper. Coherence optimization for compact PolInSAR can be performed directly in the two-dimensional observation space of the CP system without the reconstruction of the pseudo fully PolInSAR covariance matrix. The performance of different compact polarimetric modes for coherence optimization is evaluated both theoretically and experimentally and compared to the capabilities of a fully polarimetric (FP) system. It is observed that there is a significant loss in coherence of 5 10% when CP modes are available. But the trend of the coherence histograms for CP case is closed to the corresponding FP case. This study shows that the degree of coherence from a CP architecture carries enough information for PolInSAR applications.

In this paper we investigate the interferometric coherence optimization methods and develop a new procedure for the solution of the optimum coherence in case of single-mechanism using genetic algorithm. Finally, Chinese X-band airborne PolInSAR data is employed to validate the efficiency.

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.

Because of the influence of object orientation, sensor parameters and environmental factors, objects in SAR images have high changeability when the images were formed. The complex environment makes it difficult to extract object features we aim at. In this paper we take aircraft for example, propose a method to extract object features in high resolution SAR images. The features involve SURF features, numbers of engines, and the angle between engines and principal axes. Finally using the features combination we prove that the features extracted can effectively describe objects in various conditions.

Vessel detection becomes more and more important applications of remote sensing. In this paper, a vessel detection method based on gravity enhancement and multi-scale processing was applied to Synthetic Aperture Radar (SAR) images. With the method, targets' pixels were enhanced by the interactions between themselves and their neighbors, while the speckle and background clutter were suppressed after enhancement as well as the contrasts between targets and clutter had been greatly increased. In high resolution SAR image, vessel includes more pixels and can show its structure in the image. Traditional Constant False Alarm Rate (CFAR) detector which is proper for low resolution SAR image may lead to more false alarms if it is applied to high resolution image. So before segmentation, multi-scale processing was applied to avoid this problem. Cosmo-Skymed image with 3 meter pixel spacing was adopted to test the algorithm. Results show good ability for vessel detection with the high resolution SAR image.

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.

Crosstalk calibration is an important part of polarimetric calibration, and the Quegan algorithm and Ainsworth algorithm are classic algorithms to reduce crosstalk. This paper gives analysis and comparison of these algorithms for quad polarized data with low crosstalk and high crosstalk, respectively. The PolSAR data used in experiments are acquired by East China Research Institute of Electronic Engineering over the test site near Sanya City, Hainan Province of China. The results show that Ainsworth algorithm performs well for quad polarized data with low crosstalk, while the Quegan algorithm is applicable for quad polarized data with high crosstalk.

This paper presents a SAR image change detection algorithm for urban areas which mainly contains three steps: (i) modeling the Synthetic Aperture Radar (SAR) image by G0 distribution; (ii) generating a change map by computing the Kullback-Leibler divergence; (iii) masking the change map to obtain the final change areas. We propose to use the second kind statistics based parameter estimation method via Mellin transform to figure out the three parameters of G0 distribution. Experiment results indicate the second kind based G0 distribution model outperforms the moment based methods and can achieve a satisfactory result.

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.

A new method is proposed to get the segmentation threshold and detect the dark spot in oil-spill images. The method is inspired from the K -distribution model of sea background, which is widely accepted to describe the ocean clutter. By comparing the histograms of oil-spill region and the sea background, it is found that the oil slicks break the K -distribution model, but there is still some information unchanged-the relative probability ratios among the pixel values in 95%~99% CDF extent, which is used to deduce the original κ -distribution model. Finally, the intersection of the original histogram of the oil spill image and the deduced sea background PDF is selected to be the threshold. Experiment in RADARSAT-2 image shows the effectiveness of the method.

Estimation of the polarization orientation angle shifts induced by terrain azimuth slope variations is a recently developed application in radar polarimetry. In general, without any prior knowledge on the terrain, two polarimetric SAR (POLSAR) flight passes are required to derive terrain slopes in perpendicular directions for digital elevation model (DEM) generation. Moreover, we note that SAR intensity is a strong indicator of the range component of the terrain slopes. In this letter, we developed a method for DEM generation requiring only one POLSAR flight pass, by combining orientation angle estimation and a shape-from-shading technique. In particular, when limited POLSAR data are available, this POLSAR technique provides an alternative way for DEM generation. National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA/JPL) AIRSAR L-band POLSAR data over Camp Roberts, California, is used to demonstrate the results of the method proposed in this letter, and a DEM derived from simultaneously measured C-band interferometric SAR from NASA/JPL topographic SAR instrument is selected as the comparative ground truth to validate the effectiveness of this single POLSAR method. Analyses and discussions are also included in this letter.

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.

The high frequency method can deal with the metal material structure and get a good result. The other method should be used to describe the process of the shadow in the radar image. These shadows are important feature in the radar image. The stability of the shadow contour makes it helpful for the recognition process of the target body. Deterministic and statistical methods play different parts in the radar image simulation. The ray tracing techniques is constructed to describe whole simulation process. Corresponding to the MSTAR image, X band image is simulated under the same imaging condition. Comparing with the same attitude's images, the shadow contour looks similar; and the target part is well coincident too.

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.

Recently, many researches have demonstrated that polarimetric SAR interferometry is the most promising technique for forest parameter extraction. In this paper, we present a robust forest height extraction technique based on reliable phase estimation. The more accurate interferometric phase estimation can be attained with the reliable ground phase and canopy phase using three-stage inversion process and ESPRIT technique, respectively. We show the validity of the method by applying it to L-band simulated polarimetric and interferometric SAR data. The experimental results are also provided to give the availability of this method.

In this paper, we investigate a case-based reasoning (CBR) method for the classification of multi-temporal SAR images with the aid of ancillary information. Our scheme for the problem of multi-temporal SAR images classification comprises four main steps, including SAR image processing, construction of case library, case-based classification and post- classification processing. During the construction of case library, we employ a spatial-temporal analysis technique to remove fake cases, which can guarantee cases with high confidence. In the implementation of case-based classification, we propose a similarity assessment and use it for the case-based matching. After that, we investigate an object-oriented post-classification method which takes the shape of land use region into account, as a result, it leads to a more meaningful classification, and the regenerate land use image or map can be easier compared and combined with usual GIS data. Multi-temporal ENVISAT ASAR images from 2004 to 2005 are used in our experiments, where their resolution are 12.5 times 12.5 m. The study site is located in Beijing, China. During our experiments, we use the land use map of 2004 to assist the construction of the case library. The results of our experiments indicate that the CBR method is very promising for the classification of multi-temporal SAR images, where the overall classification accuracy can reach up to 80%.

The noise existed in synthetic aperture radar (SAR) image weakens the detailed features of region of interest (ROI) such as target and shadow. It also leads to the serious performance reduction of subsequent target detection, classification and recognition. The conventional regularization method could enhance target features in SAR image; however, the high computation complexity limits the real-time application of it. An improved regularization method is introduced in this paper, which increases processing speed of region feature extraction for SAR image significantly. It is theoretically proved that, by optimizing SAR projection operator, computation complexity could be reduced from O(M3N3)to O(MN) without ability losing of the region-based feature enhancement. MSTAR SAR image data is employed for algorithm experiment. The result shows that our method can increase target-to-clutter ratio significantly while restraining the noise in ROI, and then extract target and shadow from background clutters in SAR image more accurately.

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.

A wavelet based automatic targets detection method for high resolution airborne SAR data is described in this article to receive faster and more accuracy detection. This method is based on the assumption that man-made objects are easily detectable at low resolution because their scattering is more persistent than that of natural objects. The algorithm involves an improved wavelet soft threshold filter (IWSTF) and a wavelet based RCCFAR detector. In order to retain the target feature, the wavelet soft threshold filter is improved by the strategy used in the enhanced Lee filter. Instead of using a global threshold, we adopted an adaptive threshold calculated according to the detail coefficients in each scale. To accelerate the RCCFAR detector, two RCCFAR detectors are used. One is first applied to the approximate coefficients to make a coarse detection. The other one is applied to the filtered images in those regions which are regarded as candidate targets. Performance of the algorithm is assessed by some high resolution airborne SAR image and it shows that the algorithm can effectively reduce false alarms caused by speckles.

In the present paper, according to data analysis, the problem of change detection has been addressed with unsupervised change detection with multi-temporal single- channel single-polarization RADARSAT-1 SAR images. Method proposed in this paper is based on four main steps: (1) Data preprocessing. SAR images are rectified and calibrated to Gauss-Kruger projection firstly. Moreover they are co-registered to ensure the condition of good accuracy of change detection. (2) Change detection. The window log-ratio approach is adopted to compare multi-temporal SAR data, because of the multiplicative speckle in the images. Then a proper window is selected to average pixels from the two SAR data in log-ratio process instead of pixel by pixel. (3) Selection of optimal threshold value. Minimum error threshold is applied to find the optimal threshold. (4) Change detection map can be obtained by segmentation of window log-ratio image. Experiments show the approach deal well with change detection in water area.

This paper aims at ground surface deformation retrieval in wide areas including urban and suburb areas based on multi-baseline DInSAR algorithm proposed by Mora. Several progresses are made in order to extract good results. Firstly, a new complex network is presented to restrain noise influence on delaunay triangular network. Secondly, Based on a model coherence function, linear deformation velocity increments and height error increments between neighboring high coherent points (HCPs) are resolved. In order to integrate increments in network, least squares adjustment method and error controlling method are used to obtain stable parameters estimation. At last, by Combining complex and delaunay networks, wide areas deformation is investigated, from center urban areas to suburb areas. The algorithm is performed to investigate the subsidence of CangZhou City, Hebei province (China) during the time of 1993-1997 by using 9 scenes of ERS SAR data. The experiment results show serious subsidence in the region and are validated by leveling data and groundwater wells data.

In this paper, we proposed a new method for DEM generation from a single POLSAR flight pass. As usual, orientation angles induced by azimuth slope can be estimated from POLSAR data. However, for DEM generation, Another condition is need to attain the orthogonal terrain slopes. Thus, shape from shading techniques, which is mostly used by the robot vision community and generate slopes in the range direction, could then be combined with polarimetry. After terrain slopes have been computed, these slopes data are then used to solve a Possion equation to estimate the elevation surface.

Automatic target detection like oil tank detection is one of the important domains in image processing. Which could be used for disaster monitoring, oil leakage, etc. Because of differences of objects spectrums, image fusion may be used to improve the features while remain image resolution In this paper automatic oil tank detection algorithm improves recognition ration, and computational time is reduced, using improved Romanized Hough Transform, improved Canny algorithm and Fast Template Matching algorithm. Panchromatic image and multi-spectrum images of SPOT-5 satellite are used as the example. The experiment shows the algorithm reduces computational time of RHT and Fast Template Matching, and the ratio of oil tank detection exceeds 85%.

This paper concerns the Inverse Synthetic aperture radar (ISAR) image of an object over a perfectly conducting ground plane or a lossy dielectric such as sea water. The first step, the “shooting and bouncing rays” (SBR) is used to solve this problem. The next step, the wide signal bandwidths and synthetic aperture are used to resolve objects rotating about an axis normal to the line-of-sight into two-dimensional resolution cells. The results indicate that this method can efficiently get the backscattering data, and can be used to get the simulation of Synthetic aperture radar image. Keywordsradar imaging, simulation, physical optics

The application of high-resolution synthetic aperture radar (SAR) images from aerial and satellite sensors challenges the researchers for new and effective interpretation tools. Although most of the main axes in the road network may be detected by a skilled human observer looking for dark or bright linear structures, automatic detection remains a difficult task. In this paper, we proposed a new simple approach to extract main road network in high-resolution SAR images automatically. The approach is based on three steps. The first step is pre-filtering the initial SAR images by using a two-way thresholding process in order to discard uninteresting parts of image. In the second step, the results of the first step are input and use the Hough transform to identify the roads respectively. The last step is based on a feature fusion technique. The road networks detected are combined with the fusion operators. We show the improved results from some examples. It is proved that this approach is effective for the straight highways in high-resolution images

Residential area extraction from remote sensing data plays an important role for map updating. In this paper, a fused method is proposed to extract residential area information originating from China airborne SAR and optical data. First, the airborne SAR and optical images are registered. Then texture analysis, using gray level co-occurrence matrix (GLCM) texture features, is applied to the SAR image. According to those texture features, with a proper threshold, the silhouette of building area can be obtained. With the optical multispectral image, vegetation plots in residential area are extracted. The logic OR is applied to fuse the processed results of the SAR and optical images. Finally, the coverage of residential areas is overlaid on the optical image to check the effectiveness of this method.

This paper discusses spectral filtering for radar interferometry, especial the impact of the filters' position in the processing procedure. After analysis, we get the best filtering process for interferometry which contains one time of azimuth filtering and two times of range filtering. Using this process, we can improve the coherence between image pairs greatly and get an interferogram with high quality.

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

The objective of this paper is to present a basic study of rapid object recognition in full scene of high resolution SAR image. In this paper the recognition is viewed as a recipient of information from two sources: a scene containing the object to be recognized and a geo-database indicating the location of the objects to be recognized with feature class. Using the prior information offered by geo-database and SAR imaging model, the efficiency of object recognition is improved rapidly.

In this paper, a new approach to speckle filtering of synthetic aperture radar (SAR) data is presented. We define a parameter space consisting of two orthogonal subspaces-the signal subspace and the noise subspace. Then, the full polarimetric information from the signal subspace is obtained after speckle filtering. In this way, edges of different kinds of targets are preserved. The effectiveness of this method is demonstrated using the National Aeronautics and Space Administration Jet Propulsion Laboratory airborne L-band polarimetric SAR data.

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.

Unlike optical images, the microwave radiometer image contains information of ground brightness temperature. To integrate some features of microwave radiometer images and optical images, some transform schemes, such as intensity-hue-saturation (IHS), principal component analysis (PCA) and wavelet transform (WT), were applied to merge the airborne synthetic aperture radiometer images and Landsat ETM+ images. The experiments show that the wavelet based transform method shows the best potential among the three methods.

In this paper, we analyzed the relation between "noises" and interferometric synthetic aperture radar (SAR) phase unwrapping, and applied novel digital TV filtering methods to the interferograms. The filtering algorithm has less computation load than iteration anisotropic diffusion filtering and provides reliable accuracy for our phase unwrapping. The numerical results proved phase fringes protected and residues largely reduced, which lay the foundation for efficient branch-cut phase unwrapping.

Synergism of microwave active/passive data can be promising in practical applications and in the understanding of the interaction mechanism between microwave radiation and land surfaces. But compared with many of the microwave scattering and emission models, no more attention has been paid in this field. A database with simulated microwave backscattering coefficients and emissivities has been constructed under various surface roughness and soil moisture conditions by employing the IEM along with other well-known models. At first step, only Gaussian roughness distribution and small to moderate rough surface is considered. Under rougher conditions, shadowing function has been involved. By analyzing this database, we find out that in some certain ranges, microwave emissivities can be deduced from backscattering coefficients through surface rms slope and rms heights, though their relationship may not certainly be linear. And it also been shown this relationship is independent with soil water content. Therefore, by using empirical or semi-empirical passive models, soil moisture can be retrieved from backscattering coefficients with a satisfactory accuracy. Error analysis and verification using field observations is also given in the paper. By doing such a research, we hope to give an insight into the interaction mechanism between active and passive microwave sensing and also a new algorithm to retrieve land surface parameters through synergism.

Image correction and preliminary analysis of a field measurement by a C band 6-channel airborne microwave synthetic aperture radiometer is presented. This measurement was carried out along the Yellow River banks, Shaan'Xi, China, during 25/sup th/ to 30/sup th/ April, 2002. The CSSAR one dimensional C-band (6.6 GHz) 6-channel synthetic aperture radiometer was used as the airborne sensor. On board of the helicopter, a GPS receiver is also used to record the geographical positions of the flight pass. The scan was carried in over a square area across the experiment fields covering about 8/spl times/8 km/sup 2/. Ground truth observation also took place at the same time. The ground measurement parameters include: temperature of water body, air temperature, soil temperature, soil moisture at varied depth, soil surface roughness, etc. Geometric correction and registration were done before the analysis of the imagery. Some preliminary results are given.

A joint field experiment was carried out using microwave and visible sensors for bare soil and vegetation in Shunyi of Beijing in May 2001. The sensors include a microwave radiometer, microwave scatterometer and visible and infrared spectrometer. The microwave radiometer operates in the L, C and K band with H and V polarizations. The microwave scatterometer operates in the C band with HH, HV, VH, VV polarizations. The microwave brightness temperature, backscatter coefficients and reflectivity of bare soil, winter wheat and alfalfa were measured at various incidence angles. Meanwhile, ground surface parameters such as soil surface roughness, soil moisture and temperature of various depths, LAI of winter wheat and alfalfa were measured at the same time. The measured data were analyzed and an algorithm based on synergistic use of all these data is put forward to estimate soil moisture.

At present, some satellites can acquire the radiation of the long-wave infrared emitted from natural objects on the ground, and can monitor the precursory information of the change of the thermal IR before earthquake. In fact, the temperature of the Earth's surface is a result that certain factors of the Earth's surface thermal balance system affect each other. There is much uncertainty when we capture the pre-earthquake temperature abnormal information by means of satellite thermal infrared sensor and there also are many reasons to cause thermal information to be abnormal. In order to investigate containing water changing in surface soil related to recorded lot of micro-quake before earthquakes, we have carried out the experiment in laboratory. The purpose of the experiment is to explore the acoustic waves from micro-fracture before earthquake which are able to change containing water in surface soil, so as to simulate the thermal abnormal picture on the remote sensing image related to a earthquake. The result of the experiment shows that the containing water in the surface soil originally taken on the natural decreasing trend in the spontaneous evaporation situation, as soon as the acoustic wave enters into the surface soil then the containing water is up. From the experiment result, we proposed that the change of water content in the Earth surface soils maybe was the one of mainly reasons that could be caused the thermal infrared radiation precursory information on the remote sensing images by the earthquake activity.

This paper describes a novel variational optimization model with anisotropic diffusion for interferometric synthetic aperture radar (SAR) phase unwrapping, which is derived with the use of a global cost function to minimize the phase discontinuities in the unwrapped phase map and reduce noise. Numerical results proved that our phase unwrapping algorithm is robust and reliable on high noisy phase data with discontinuation.

In the process of unwrapping phase images with high noise, traditional algorithms and derivative methods have many problems: for example, the error of noise region is transferred to other regions, and affects the extraction of useful information. In order to resolve this problem, we realize a phase unwrapping method on the minimum cost flows algorithm based on irregular network. This method is that low-quality phases are eliminated from interferogram and only high-quality phases are processed, so it can avoid the effect that low-quality are processed, so it can avoid the effect that low-quality regions make on high-quality ones, and assure phase unwrapping of the high-quality regions, then acquire good results.

Polarimetric SAR interferometry is much more sensitive to the distribution of oriented objects in a vegetated land surface than either polarimetric or interferometry alone. In this paper, we propose a polarimetric SAR interferometry technique for the estimation of parameters characterizing the vertical structure vegetated land surfaces. Based on simple physical model, we use SIR-C/X-SAR full polarimetric data to calculate the optimized interferometric coherences. The test site is Tianshan and its surrounding area, where pine trees are planted and the characteristics are well known. As a result, we could estimate the tree height distribution, and the quantitative evaluation is presented to relate the phase center differences and the tree types and the polarization combinations.