High Resolution SAR Images Research Articles

AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

Abstract. Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Simulation method of high-resolution spaceborne SAR imaging for the South-to-North transmission line

Simulation method of high-resolution spaceborne SAR imaging for the South-to-North transmission line

High-resolution Slide Spotlight SAR Imaging by BP Algorithm and Heterogeneous Parallel Implementation

High-resolution Slide Spotlight SAR Imaging by BP Algorithm and Heterogeneous Parallel Implementation

A Backprojection-Based Imaging for Circular Synthetic Aperture Radar

Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. However, the CSAR image focusing is affected by the motion deviations of platform. In the processing of experimental CSAR data to deal with motion errors, the main way is using setup calibrators, which restricts its widespread applications. In this paper, based on the estimation of motion errors, an autofocus CSAR imaging strategy is proposed without using any setup calibrator. The first step is to split the entire aperture into several subapertures, the second step is to process the data in subapertures with an autofocus backprojection method, and the last step is to obtain the final CSAR image by merging the subimages obtained from the subaperture processing. The CSAR data processing results prove that the proposed strategy can remove the motion errors accurately and acquire well-focused CSAR images.

Building extraction from high-resolution SAR imagery based on deep neural networks

ABSTRACTSynthetic aperture radar (SAR) imagery classification is considered as one of the most significant SAR-based application. Developing SAR imagery classification applications based on new datasets requires considerable amount of work, such as feature extraction and validation. The classification processing flow is complex and should be specially designed for different SAR images. To reduce the complexity and improve the processing efficiency, a unified processing scheme based on the deep neural networks (DNN) is proposed. The scheme can be applied to most SAR imagery classification tasks by simply adjusting the model parameters. The proposed scheme is employed to extract building areas from different high resolution SAR images obtained by different sensors based on fully connected feedforward deep network (FDN) and convolutional neural network (CNN). The study results indicate that the proposed classification scheme has high accuracy and efficiency.

Adaptive Neighborhood Selection Semi-supervised Discriminative Locality Alignment Based Urban Building Areas Extraction from High-Resolution SAR Image

Adaptive Neighborhood Selection Semi-supervised Discriminative Locality Alignment Based Urban Building Areas Extraction from High-Resolution SAR Image

P60 高分解能SAR画像を用いた火山地形の判読(ポスターセッション)

P60 高分解能SAR画像を用いた火山地形の判読(ポスターセッション)

高分解能衛星SAR画像の変化抽出に基づく津波による橋梁被害把握

高分解能衛星SAR画像の変化抽出に基づく津波による橋梁被害把握

BEMD-based high resolution image fusion for land cover classification: A case study in Guilin

Analysis of image texture feature can help to reduce the adverse effects of the condition that same object but different band or same band but different object. Therefore, if it can add and highlight the texture information to the remote sensing image, it will be very helpful in the classification of ground objects. In this paper we consider to add SAR image information in classification. Bidimensional empirical mode decomposition (BEMD) has been widely applied to the analysis of non-stationary and non-linear signals. This paper proposes a new method for fusing high resolution SAR and optical image in Guilin area, based on Bidimensional empirical mode decomposition (BEMD) method.

Absolute geolocation accuracy of high-resolution spotlight TerraSAR-X imagery – validation in Wuhan

TerraSAR-X (TSX) can acquire high-resolution SAR images and due to its high orbit precision as well as its ability to acquire data from different off-nadir viewing angles, the high-precision stereo geolocation can be obtained. In this study, we investigate the absolute geolocation accuracy of TSX high-resolution images in Wuhan, China. We present a direct stereo SAR geolocation method and analyze the 2D and 3D geoposition accuracy of two corner reflectors. The sub-meter localization accuracy was achieved using only atmospheric correction information available in the TSX metadata.

Noise Reduction of High-Resolution SAR Image over Vegetation and Urban Areas

aperture radar (SAR) has been commonly used in many civilian and military application, and the SAR with moving targets indication is a very hot topic in recent years. As a lot of literatures discussed, if the returns from moving target are method in the same way as the motionless returns, the moving object will appear as an azimuth shift due to range motion and the image of the target will be messy in the azimuth direction due to azimuth motion .The high resolution interferograms above vegetation or urban area are varied, which will break the local stationary theory and make it difficult to get a large number of independent and identically distributed samples for interferometric noise suppression.

Characteristic function based parameter estimation of skewed alpha-stable distribution: An analytical approach

This paper introduces a new technique for analytical parameter estimation of skewed α-stable distribution with 1<α≤2. Stable distribution as a four-parameter non-Gaussian distribution is completely characterized by its characteristic function (CF). There are some serious limitations in parameter estimation of α-stable distribution due to the lack of closed-form expression for the general α-stable probability density function (PDF). The proposed estimator uses a hierarchical framework based on the skewed α-stable CF, and hence, allows a rapid estimation of parameters with high accuracy in real-time signal processing algorithms. In our scheme, only two values of α-stable CF, which has analytic formula, are utilized to estimate the parameters of α-stable density. In addition, the closed-form expression for estimating the required values of CF is derived. To provide a precise quantitative assessment, our proposed approach is compared with three other state-of-the-art estimators which have analytic formulas through a series of goodness-of-fit tests. Simulation results also demonstrate that the proposed method has a good accuracy both for the symmetric and non-symmetric (skewed) α-stable distributions. Furthermore, the advantage of the proposed CF based method becomes more evident through the experimental results obtained from the high-resolution SAR images.

Statistical Modeling of PMA Detector for Ship Detection in High-Resolution Dual-Polarization SAR Images

The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique of the PMA detector is desirable for practical applications, wherein a crucial problem is to find an appropriate model to describe the PMA statistics for varied sea surfaces. First, we consider a new probability density function to characterize the PMA statistics of homogeneous sea surfaces. Second, by using the new density and multiplicative model, the PMA detector's statistical model for nonhomogeneous sea surfaces is specified and demonstrated to be the $\mathcal{G}^{0}$ distribution. Then, a theoretical analysis of the relationship between the performance of the standard CFAR detection and the parameters in the $\mathcal{G}^{0}$ distribution is conducted. Experiments performed on the measured RADARSAT-2 and NASA/JPL AIRSAR images verify the effectiveness and appropriateness of the $\mathcal{G}^{0}$ model for describing the statistical behavior of the PMA of sea clutter, as well as the usefulness of the model for practical ship-detection applications.

ON THE CHALLENGES IN STEREOGRAMMETRIC FUSION OF SAR AND OPTICAL IMAGERY FOR URBAN AREAS

Abstract. This paper discusses the challenges arising if SAR and optical imagery shall be fused for stereogrammetric 3D analysis of urban areas. In this context, a concept for SAR and optical data fusion is presented, which is meant to enable the reconstruction of urban topography independent of the type of the available data. This fusion is modelled in a voxelized object space, from which 3D hypotheses are projected into the available datasets. Among those hypotheses then the one showing the greatest SAR-optical similarity is chosen to be the reconstructed 3D point. Within first experiments, it is shown that the determination of similarity between high-resolution SAR and optical images is the major challenge within the framework of the proposed concept. After this challenge has been solved, the proposed method is expected to allow 3D reconstruction of urban areas from SAR-optical stereogrammetry for the first time. It is expected to be beneficial, e.g., for rapid mapping tasks in disaster situations where optical images may be available from geodata archives, but instantaneous data can only be provided by daylight- and weather-independent SAR sensors.

ON THE CHALLENGES IN STEREOGRAMMETRIC FUSION OF SAR AND OPTICAL IMAGERY FOR URBAN AREAS

This paper discusses the challenges arising if SAR and optical imagery shall be fused for stereogrammetric 3D analysis of urban areas. In this context, a concept for SAR and optical data fusion is presented, which is meant to enable the reconstruction of urban topography independent of the type of the available data. This fusion is modelled in a voxelized object space, from which 3D hypotheses are projected into the available datasets. Among those hypotheses then the one showing the greatest SAR-optical similarity is chosen to be the reconstructed 3D point. Within first experiments, it is shown that the determination of similarity between high-resolution SAR and optical images is the major challenge within the framework of the proposed concept. After this challenge has been solved, the proposed method is expected to allow 3D reconstruction of urban areas from SAR-optical stereogrammetry for the first time. It is expected to be beneficial, e.g., for rapid mapping tasks in disaster situations where optical images may be available from geodata archives, but instantaneous data can only be provided by daylight- and weather-independent SAR sensors.

MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU

Abstract. Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.

MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU

Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.

A METHOD FOR THE EXTRACTION OF LONG-TERM DEFORMATION CHARACTERISTICS OF LONG-SPAN HIGH-SPEED RAILWAY BRIDGES USING HIGH-RESOLUTION SAR IMAGES

Natural causes and high-speed train load will result in the structural deformation of long-span bridges, which greatly influence the safety operation of high-speed railway. Hence it is necessary to conduct the deformation monitoring and regular status assessment for long-span bridges. However for some traditional surveying technique, e.g. control-point-based surveying techniques, a lot of human and material resources are needed to perform the long-term monitoring for the whole bridge. In this study we detected the long-term bridge deformation time-series by persistent scatterer interferometric synthetic aperture radar (PSInSAR) technique using the high-resolution SAR images and external digital elevation model. A test area in Nanjing city in China is chosen and TerraSAR-X images and Tandem-X for this area have been used. There is the Dashengguan bridge in high speed railway in this area as study object to evaluate this method. Experiment results indicate that the proposed method can effectively extract the long-term deformation of long-span high-speed railway bridge with higher accuracy.

A METHOD FOR THE EXTRACTION OF LONG-TERM DEFORMATION CHARACTERISTICS OF LONG-SPAN HIGH-SPEED RAILWAY BRIDGES USING HIGH-RESOLUTION SAR IMAGES

Abstract. Natural causes and high-speed train load will result in the structural deformation of long-span bridges, which greatly influence the safety operation of high-speed railway. Hence it is necessary to conduct the deformation monitoring and regular status assessment for long-span bridges. However for some traditional surveying technique, e.g. control-point-based surveying techniques, a lot of human and material resources are needed to perform the long-term monitoring for the whole bridge. In this study we detected the long-term bridge deformation time-series by persistent scatterer interferometric synthetic aperture radar (PSInSAR) technique using the high-resolution SAR images and external digital elevation model. A test area in Nanjing city in China is chosen and TerraSAR-X images and Tandem-X for this area have been used. There is the Dashengguan bridge in high speed railway in this area as study object to evaluate this method. Experiment results indicate that the proposed method can effectively extract the long-term deformation of long-span high-speed railway bridge with higher accuracy.

Locality-constraint discriminant feature learning for high-resolution SAR image classification

It remains one of the most challenging tasks to distinguish different terrain materials from a single SAR image. With the increase of ground resolution, it allows us to model the SAR image directly by exploiting spatial structures and texture information that are extracted by several machine learning approaches. In this paper, a novel feature learning approach is proposed to capture discriminant features of high-resolution SAR images. In the first stage, a weighted discriminant filter bank is learned from some labeled SAR image patches to generate low-level features. Then, the locality constraint is introduced to produce the high-level features in both the encoding and the spatial pooling procedure. In this work, the superpixels are employed as the basic operational units instead of the pixels for terrain classification. With some learned domain patterns which are learned from all of the high-level features of each pixel, the superpixel is characterized by a hyper-feature. In the last stage, a linear-kernel support vector machine is utilized to classify all of these hyper-features which are generated for each superpixel. The experimental results show a better classification performance of the proposed approach than several available state-of-the-art approaches.