- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Reconstruction of Full-Pol SAR Data from Partialpol Data Using Deep Neural Networks
摘要： We propose a deep neural networks based method to reconstruct full polarimetric (full-pol) information from single polarimetric (single-pol) SAR data. It consists of two parts: feature extractor which is used to obtain multi-scale multi-layer features of targets in single-pol gray image, and feature translator that converts the geometric features to defined polarimetric feature space. The proposed method is demonstrated on L-band UAVSAR of NASA/JPL images over San Diego, CA, and New Orleans LA, USA. Both qualitative and quantitative results show the reconstructed full-pol images agree well with true full-pol images, the proposed networks have a good spatial robustness. Model-based target decomposition and unsupervised classification can be used directly on constructed full-pol images.
关键词： Deep Neural Network,unsupervised classification,Polarimetric Synthetic Aperture Radar (PolSAR),SAR image colorization
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Omega-K Algorithm Based on Series Reversion and Least Square for High-Resolution Spaceborne SAR
摘要： When processing high-resolution spaceborne synthetic aperture radar (SAR) data, the orbit curvature is a key aspect that must be taken into account. The non-hyperbolic range history makes most SAR imaging approaches not suitable for the curved orbit. Based on the two-dimensional spectrum derived by series reversion (SR), a modified Omega-K algorithm (OKA) is proposed in this paper. Making use of the reference function calculated by SR, an accurate bulk compression is implemented. Following, a modified Stolt interpolation is applied based on least square (LS), to perform the residual range-variant processing efficiently. The method described can achieve satisfactory focusing results for spaceborne SAR, without a large number of computation. Point targets simulations have validated the presented research.
关键词： curved orbit,Omega-K algorithm,Spaceborne synthetic aperture radar,series reversion,least square
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Integration of SAR and GEOBIA for the Analysis of Time-Series Data
摘要： In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.
关键词： self-organizing map clustering,classification,object-based image analysis,multitemporal synthetic aperture radar
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - SAR Patch Categorization Using Stacked Sparse Coding
摘要： This paper presents Synthetic Aperture Radar (SAR) patch categorization using unsupervised feature learning framework. It is based on layer based sparse coding, which extends a sparse coding to a multilayer architecture. A contribution of this paper is a framework which consists of 3 layers of sparse coding, local spatial pooling layer, normalization layer, map reduction layer and a classification layer. The new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. The proposed method achieved promising results in SAR patch categorization.
关键词： classification,Synthetic Aperture Radar,sparse coding,Categorization
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Car-Borne SAR System for Interferometric Measurements: Development Status and System Enhancements
摘要： Terrestrial radar systems are used operationally for area-wide measurement and monitoring of surface displacements on steep slopes, as prevalent in mountainous areas or also in open pit mines. One limitation of these terrestrial systems is the decreasing cross-range resolution with increasing distance of observation due to the limited antenna size of the real aperture radar or the limited synthetic aperture of the quasi-stationary SAR systems. Recently, we have conducted a first experiment using a car-borne SAR system at Ku-band, demonstrating the time-domain back-projection (TDBP) focusing capability for the FMCW case and single-pass interferometric capability of our experimental Ku-band car-borne SAR system. The cross-range spatial resolution provided by such a car-based SAR system is potentially independent from the distance of observation, given that an adequate sensor trajectory can be built. In this paper, we give (1) an overview of the updated system hardware (radar setup and high-precision combined INS/GNSS positioning and attitude determination), and (2) present SAR imagery obtained with the updated prototype Ku-band car-borne SAR system.
关键词： azimuth focusing,Ku-band,SAR imaging,ground-based SAR system,car-borne SAR,parallelization,SAR interferometry,GPU,CUDA,interferometry,CARSAR,Synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Feature Design for Classification from Tomosar Data
摘要： While previous work primarily focused on using Tomographic Synthetic Aperture Radar (TomoSAR) data to analyze the 3D structure of the imaged scene, we study its potential for the generation of semantic land cover maps in a supervised framework. We extract different features from the covariance matrices of a tomographic image stack as well as from the tomograms computed by tomographic focusing. To assess the impact of our approach, we compare our results to classification maps obtained from a fully polarimetric image. We show that it is possible to outperform classification results from polarimetric data by carefully designing hand-crafted features which can be extracted either from multi-baseline single polarization covariance matrices or from tomograms obtained after tomographic focusing. Our experiments show a significant gain in the classification accuracy, especially on challenging classes such as heterogeneous city and road.
关键词： machine learning,Synthetic Aperture Radar,feature extraction,tomography
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Highly Squinted Imaging for Diving SAR with 3-Dacceleration
摘要： A uniform linear motion is generally assumed in traditional synthetic aperture radar(SAR) processing algorithms. However, it is inevitable that if there is 3-D acceleration, the image formulation processing complexity will be significantly increased, especially in highly squinted situations. This paper aims to handle the imaging problems in the highly squinted SAR with three-dimensional acceleration, which utilizes the Taylor formula to expand the distance equation and further resolve it. Based on the formulated range formula, we further utilize the two-dimensional non-uniform Fast Fourier Transform (2D-NUFFT) to obtain focused SAR imagery. The proposed methods are verified with simulation results.
关键词： 2D-NUFFT algorithm,highly squinted,synthetic aperture radar (SAR),nonuniform fast Fourier transform (NUFFT),3-D acceleration model
更新于2025-09-23 15:22:29
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High-Resolution Three-Dimensional Displacement Retrieval of Mining Areas From a Single SAR Amplitude Pair Using the SPIKE Algorithm
摘要： High-resolution three-dimensional (3-D) displacements of mining areas are crucial to assess mining-related geohazards and understand the mining deformation mechanism. In 2018, we proposed a cost-effective and robust method for retrieving mining-induced 3-D displacements from a single SAR amplitude pair (SAP) using offset tracking (OT) procedures. Hereafter, we refer to this method as the 'alternative OT-SAP' (AOT-SAP) method. A key step in the AOT-SAP method is solving the 3-D surface displacements from the AOT-SAP-constructed linear system using routine lower–upper (LU) factorization. However, if the AOT-SAP method is used to retrieve high-resolution 3-D displacements, the dimension of the linear system becomes very large (in the order millions), and a high-end supercomputer is often needed to perform the LU-based solving procedure. This significantly narrows the practical application of the AOT-SAP method, considering the limited availability of supercomputers. In this paper, owing to the banded nature of the AOT-SAP-constructed linear system, we introduce the SPIKE algorithm as an alternative to LU factorization to solve high-resolution mining-induced 3-D displacements. The SPIKE algorithm is a divide-and-conquer direct solver of a large banded system, which can parallelly or sequentially solve a large banded linear system, with a much smaller memory requirement and a shorter time cost than LU factorization. This allows us to retrieve the high-resolution 3-D mining-induced displacements with the AOT-SAP method on either a supercomputer or a standard personal computer. Finally, the accuracy of the retrieved 3-D displacements and the efficiency improvement of the SPIKE algorithm were tested using both simulation analysis and a real dataset.
关键词： large banded system,offset tracking (OT),underground mining,SPIKE algorithm,three-dimensional (3-D) displacements,Interferometric synthetic aperture radar (InSAR)
更新于2025-09-23 15:22:29
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Nonlocal Compressive Sensing-Based SAR Tomography
摘要： Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.
关键词： interferometric synthetic aperture radar (InSAR),tomographic SAR (TomoSAR),Compressive sensing (CS),nonlocal (NL) filtering
更新于2025-09-23 15:22:29
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Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery
摘要： Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm “glocal” Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the “glocal” IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated.
关键词： RADARSAT-2,classification,lake ice,synthetic aperture radar
更新于2025-09-23 15:22:29