- 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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Investigation of Tandem-x Penetration Depth Over the Greenland Ice Sheet
摘要： Ongoing global warming causes dramatic changes globally, especially with respect to Polar Regions. In this context, digital elevation data is of high importance for most glaciological applications. In this paper, we investigate TanDEM-X penetration depth over snow and ice on the Greenland ice sheet. In particular, the relation of backscatter intensity and interferometric coherence to penetration depth of the X-band InSAR signal is explored in order to improve the reliability of TanDEM-X elevation data. The analyses showed a distinct relationship of backscatter intensity, coherence and penetration depth. In addition, the influence of the height of ambiguity of the interferometric TanDEM-X data is presented. On an experimental test site in Northern Greenland, we demonstrated the estimation of TanDEM-X penetration depth based on backscatter intensity and interferometric coherence utilizing a linear regression model.
关键词： TanDEM-X,Interferometric Synthetic Aperture Radar (InSAR),Greenland ice sheet,penetration depth
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Locality-Constrained and Class-Specific Sparse Representation for Sar Target Recognition
摘要： Recently, sparse representation has achieved the impressive performance on target recognition in synthetic aperture radar (SAR) image. However, the unstable and unsupervised optimization of the sparse representation may lead to undesired recognition result. In this paper, a locality-constrained and class-specific sparse representation (LCSR) framework is presented to alleviate these problems. Instead of the sparse constraint, the locality constraint is designed to utilize the local structure information of the training samples. It provides stable representation for the samples with minor variations, which is beneficial to classification. To further improve the recognition performance, the query sample is represented as a linear combination of class-specific galleries based on the supervision of class information. The inference is reached corresponding to the class with the minimum reconstruction error. The experimental results demonstrate the effectiveness and robustness of the proposed method.
关键词： class-specific,sparse representation,target recognition,locality constraint,synthetic aperture radar
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Extended Puma Algorithm for Multibaseline SAR Interferograms
摘要： Phase unwrapping (PU) is one of the key process in reconstructing the digital elevation model (DEM) of a scene from its interferometric synthetic aperture radar (InSAR) data. Compared with traditional single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, which can be applicable to reconstruct the DEM where topography varies drastically. However, the performance of the multibaseline PU is directly concerned with noise level. Contrarily, the single-baseline PU algorithm has good noise robustness, since it is based on the globe wrapped phase information, such as PU-max-flow (PUMA) algorithm. In order to improve the noise robustness of the multibaseline, in this paper, we extend single-baseline PUMA algorithm to multibaseline domain, referred to as multibaseline PUMA algorithm, which allows the unwrapping of multibaseline interferograms for the generation of DEM. The proposed algorithm does not need to obey the phase continuity assumption by taking the advantages of multibaseline diversity and improves the noise robustness by using the global wrapped information both from single- and multibaseline domain. The performance of the proposed algorithm is tested on simulated InSAR data experiments, which demonstrate the effectiveness and noise robustness of the proposed algorithm.
关键词： Robust,Multibaseline,Phase unwrapping-max-flow (PUMA),Interferometric synthetic aperture radar (InSAR)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Moving Target Detection and Imaging for Geosynchronous SAR
摘要： Compared with stationary targets imaging, detection and imaging of moving targets becomes more difficult in Geosynchronous synthetic aperture radar (GEOSAR), especially when the moving target is drowned in strong ground clutter. In this paper, a scheme of moving target detection and imaging with three antennas is proposed for GEOSAR. Firstly, the displaced phase center antenna (DPCA) is used to eliminate stationary targets’ echo. Then, the third-order keystone transform is applied to correct the range migration. Next, after coherent integration, the constant false alarm ratio (CFAR) is used to detect the moving target. Finally, simulation results demonstrate the effectiveness of the proposed scheme.
关键词： clutter elimination,displaced phase center antenna (DPCA),Geosynchronous synthetic aperture radar (GEOSAR)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Aircraft Detection in Sar Images Using Saliency Based Location Regression Network
摘要： In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network （CNN） framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could e?ectively obtain a more accurate detection result.
关键词： Synthetic Aperture Radar,Data Augmentation,Convolutional Neural Network,Aircraft Detection
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Simulation of Isar Motion Compensation for Moving Targets Based on Particle Swarm Optimization
摘要： In inverse synthetic aperture radar (ISAR) imaging, the imaging results can be affected by the unexpected target motions. This results in a blurry and unrecognizable image. The motion parameters estimation is a compensation method to improve the ISAR image refocusing and quality. In this study, the backscattered echo signals are simulated by the linear geometry system of ISAR moving targets. The entropy of ISAR image is used as a criterion to evaluate the image quality. Furthermore, this entropy measure can be treated as a cost function of the particle swarm optimization (PSO) method and minimized by PSO to improve the quality of ISAR images. The experimental results showed that our proposed PSO motion estimation approach to entropy minimization for ISAR imaging can not only efficiently improve the estimation capability of motion parameters, but also significantly achieve a better performance of ISAR image refocusing.
关键词： motion compensation,entropy minimization,particle swarm optimization (PSO),inverse synthetic aperture radar
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Joint Model for Isolating Stratified Tropospheric Delays in Multi-Temporal Insar
摘要： Stratified tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) result from the temporal variation of vertical stratification in the lower part of the troposphere. Although an approximately model can be made by assuming a linear relationship between topography and delayed phase in the interferogram, the estimation is weakened by the spatial variability of troposphere and the interference from other confounding signals (e.g., deformation, topographic error and orbit error, etc.). In this contribution, a jointly tropospheric correction scheme is proposed to simultaneously estimate stratified tropospheric delays with deformation and topographic errors. Spatial variability of tropospheric properties is addressed through a localized estimation which is derived by quadtree segmentation according to height gradient. The performance of the proposed method is validated and compared with the conventional linear and weather-model-based methods using Sentinel-1 dataset.
关键词： least squares,Stratified tropospheric delays,Interferometric Synthetic Aperture Radar (SAR) (InSAR)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Polarization Feature Extraction Using Quaternion Neural Networks for Flexible Unsupervised Polsar Land Classification
摘要： We propose an unsupervised PolSAR land classification system consisting of quaternion auto-encoder and quaternion self-organizing map (SOM). Most of the conventional methods extract features necessary for the land classification based on a few of scattering models predefined by human beings. However, we cannot expect classification into a large number of land categories recognizable to humans by using such restricted features. In this paper, we propose a method employing quaternion auto-encoder and quaternion SOM for feature extraction and classification, respectively. As a result, we succeed in discovering new and more detailed land categories. For example, town areas are divided into residential areas and factory sites.
关键词： Poincare parameter,quaternion neural network,auto-encoder,unsupervised classification,Polarimetric synthetic aperture radar (PolSAR),self-organizing map (SOM),land classification
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Target Aspect Identification in SAR Image: A Machine Learning Approach
摘要： Identifying the aspect for a given target is an important issue in synthetic aperture radar (SAR) image interpretation. A new SAR target aspect identification method based on machine learning theory is proposed in this paper. First, the aspect angles of the SAR target are discretized, and the spatial relationships of the neighborhoods of the SAR target samples are established. Then an optimal linear mapping is solved based on the proposed subspace aspect discriminant analysis. The samples will be projected into a low-dimensional space and be of a better aspect identifiability than in their original space. Finally, the projected samples are fed into a multi-layer neural network, and the aspects of the SAR targets will be indicated. Experimental results have shown the superiority of the proposed method based on the moving and stationary target acquisition and recognition (MSTAR) dataset.
关键词： machine learning,Synthetic aperture radar,multi-layer neural network,target aspect identification
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimating the NDVI from SAR by Convolutional Neural Networks
摘要： Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.
关键词： synthetic aperture radar (SAR),Data fusion,multitemporal,deep learning,vegetation monitoring
更新于2025-09-10 09:29:36