- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[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 - Significant Wave Height Retrieval from Gaofen-3 Wave Mode Images
摘要： Significant wave height (Hs), is an important parameter, represented as the integration of directional wave spectra. Although many researchers have directly extracted Hs from SAR images and got a great accuracy of retrieval, those approaches are not suitable for GF-3 SAR data. In this paper, we propose an empirical approach for SAR Hs retrieval, using λc estimated from the real part of image cross spectra obtain from VV-polarized Gaofen-3 (GF-3) wave mode data acquired in different radar beams (called wave-code). Results using GF-3 wave mode data from January to February 2017 indicate that the bias and RMSE errors are: 189 wave-code, -0.13 m and 0.57 m; 190 wave-code, -0.07 m and 0.34 m; 193 wave-code, -0.3 m and 0.59 m; 199 wave-code, 0.16 m and 0.68 m; 215 wave-code, 0.2 m and 0.87 m. they show a relative behavior between the retrieved Hs and the Hs extracted from WAVEWATCH-III (WW3). However, there is a significant error when WW3-extracted Hs exceed 4 m. It seems that the model is not suitable for Hs retrieval on high sea conditions.
关键词： wave empirical retrieval,GF-3,cutoff wavelength estimation,synthetic aperture radar,wave mode
更新于2025-09-23 15:22:29
-
An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification
摘要： Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and usually large scale. Fast and accurate classification is particularly important for their applications. By introducing online learning, the PolSAR system can learn a classification model incrementally from a stream of instances, which is of high efficiency for newly arrived samples processing, strong adaptability for a dynamically changing environment, and excellent scalability for rapidly increasing data. In this paper, we propose an Online Multi-view Passive-Aggressive learning algorithm, named OMPA, for PolSAR data real-time classification. The polarimetric, color, and texture features are extracted to characterize PolSAR data, and each type of features corresponds to one view. In order to exploit the consistency and complementary property of these views, we give a new optimization model that ensembles the classifiers of multiple distinct views and enforces the agreement between each predictor and the combined predictor. The corresponding algorithms for both binary and multiclass classification tasks are derived, and the update steps have analytical solutions. In addition, we rigorously derive a bound on the number of prediction mistakes of the method. The proposed OMPA algorithm is evaluated on two real PolSAR datasets for built-up areas extraction and land cover classification, respectively. Experimental results demonstrate that OMPA consistently maintains a smaller mistake rate with low time cost and achieves about 1% and 2% accuracy improvements on the datasets, respectively, compared with the best results of the previously known online single-view and multiview learning methods.
关键词： polarimetric synthetic aperture radar (PolSAR),Multiview learning,passive-aggressive (PA) algorithm,online classification
更新于2025-09-23 15:22:29
-
PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme
摘要： In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant co-polar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.
关键词： Coherency matrix,polarimetric synthetic aperture radar (PolSAR),cross-polarization,unitary matrix rotation,land-cover classification
更新于2025-09-23 15:22:29
-
[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network
摘要： Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts: a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.
关键词： synthetic aperture radar (SAR),deep neural network (DNN),polarimetric reconstruction
更新于2025-09-23 15:22:29
-
[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - A High Frequency Vibration Compensation Approach in Terahertz SAR Based on Wavelet Multi-Resolution Analysis
摘要： The use of terahertz wave in SAR imaging can solve the difficulties of frame rate and detection of slow moving targets in conventional SAR imaging. The most important difference between terahertz SAR (THz-SAR) and conventional SAR is the treatment on motion compensation. For reason that the wavelength of terahertz SAR is much shorter than that of conventional microwave SAR, the tiny vibration of SAR platform will blur SAR images, especially high frequency components. The high frequency vibration will result in paired echoes in SAR imaging, which can't be focused with traditional SAR imaging algorithms. Thus the vibration parameters can't be estimated precisely enough to construct the reference function to compensate the sinusoidal modulation phase. So we first get focused paired echoes in terahertz SAR imaging through Doppler keystone transform (DKT), then we propose a frequency estimation method based on wavelet multi-resolution analysis, along with parametric space projection method, to complete the high frequency vibration estimation of terahertz SAR. At last, the numerical tests using the point target echoes validate the proposed method.
关键词： synthetic aperture radar,high frequency vibration error,Terahertz,wavelet multi-resolution analysis,vibration estimation
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - An Object-Based Method Based on a Novel Statistical Distance for SAR Image Change Detection
摘要： This paper introduces an object-based method based on a new statistical distance for SAR image change detection. Firstly, multi-temporal segmentation is carried out to segment two temporal SAR images simultaneously. It considers the homogeneity in two temporal images, and could generate homogeneous objects in spectral, spatial and temporal. In addition, through setting different segmentation parameters, the multi-temporal images can be segmented in a set of scales. This process exploits the advantages of OBIA that could effectively reduce spurious changes, and considers the scale of change detection task. Secondly, a multiplicative noise model called Nakagami–Rayleigh distribution is employed to describe SAR data, and then applied to Bayesian formulation. Thus, a new statistical distance that is insensitive to speckles is derived to measure the distances between pairs of parcels. Then, cluster ensemble algorithm is utilized to improve accuracy of individual result in each scale to obtain the final change detection map. Finally, multi-temporal Radarsat-2 images are employed to verify the effectiveness of the proposed method compared with other four methods.
关键词： synthetic aperture radar (SAR),multi-scale analysis,object-based image analysis,change detection
更新于2025-09-23 15:22:29
-
[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
-
[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
-
[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
-
[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