- 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 - A Fast Sparse Representation Method for SAR Target Configuration Recognition
摘要： Focusing on the problem of the real-time implementation in sparse representation (SR) based recognition algorithm, a fast sparse representation (FSR) algorithm is presented in this paper to improve the efficiency of synthetic aperture radar (SAR) target configuration recognition. Taking the inertia variance characteristic of SAR target images over a small range of azimuth angles into consideration, training samples of each configuration are averaged. Instead of using all the training samples to establish the dictionary in SR, the average samples are utilized to construct the dictionary in FSR. A small dictionary accelerates the speed of the proposed algorithm.
关键词： sparse representation (SR),Synthetic aperture radar (SAR) images,target configuration recognition
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 - Fully Convolutional Network with Polarimetric Manifold for SAR Imagery Classification
摘要： Image classification performance depends on the understanding of image features and classifier selection. Owing to the special imaging mechanism, achieving precise classification for remote sensing imagery is still quite challenging. In this paper, a fully convolutional network with polarimetric manifold, is proposed for Synthetic Aperture Radar (SAR) image classification. First, the polarimetric features are extracted to describe the target information; then the feature points in high-dimension are mapped to low-dimension through the manifold structure. In this way, the effect of single manifold is equal to that of multi-layer convolution. The experimental results on SAR image data indicate that the presented manifold network can effectively separate the polarimetric features and improve the classification accuracy.
关键词： manifold structure,Synthetic Aperture Radar (SAR),image classification,convolution network
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 - Spaceborne Bistatic SAR Scene Simulation
摘要： Augmenting traditional spaceborne SAR sensors with additional receive-only satellites in close formation enhances the observation space, allowing for single-pass interferometry in along- and/or across-track with flexible baselines and the potential to build tomographic stacks with reduced temporal decorrelation properties. The feasibility of such add-ons is presently investigated by ESA and other national space agencies and for a variety of master satellites operating from X- to L-band. This paper presents a simulation framework for complete scenes dedicated specifically to the analysis of the additional technical requirements imposed by the bistatic SAR imaging geometry with relatively large along-track separation of illuminating master/chief and the receive-only slaves/deputies.
关键词： synchronisation,bistatic 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 - Towards the Retrieval of 2-D Vessel Velocities with Single-Platform Spaceborne SAR: Experimental Results with the TerraSAR-X 2-Looks Tops Mode
摘要： In this contribution, we propose the use of 2-looks Synthetic Aperture Radar data to retrieve azimuth velocities of moving targets with a single platform. The established technique to retrieve velocities of moving targets is Along-track Interferometry (ATI), which provides a measurement of the velocity in the radar line of sight direction by employing two or more phase centers separated in the along-track direction. The use of 2-looks data allows to observe targets at two different instants of time, with a Doppler separation, enabling the retrieval of the target velocity in the azimuth direction. We introduce the 2-looks Terrain Observation by Progressive Scans (TOPS) mode, presenting the performance that can be achieved with the TerraSAR-X system. Moreover, we present experimental results with real data acquired with TerraSAR-X over coastal areas to retrieve velocities of vessels. A validation of the results with Automatic Identification System (AIS) data (ground truth) provides accuracies below 1 m/s.
关键词： Synthetic Aperture Radar (SAR),Vessel tracking,2-looks TOPS,TerraSAR-X
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 - 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