- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Co-polarization channel imbalance phase estimation by corner-reflector-like targets
摘要： Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as "CR-like targets", can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions.
关键词： Corner reflector,Polarimetric synthetic aperture radar,Calibration,Target detection
更新于2025-09-23 15:23:52
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A Reflection Symmetry Approximation of Multilook Polarimetric SAR Data and its Application to Freeman-Durden Decomposition
摘要： Freeman–Durden decomposition is a frequently used technique to analyze the scattering characteristics of multilook Polarimetric Synthetic Aperture Radar (POLSAR) data. When it is applied to the real POLSAR data, two problems emerge, namely, the volume scattering overestimation and negative powers. Many researchers think these two problems are caused by the insufficient decomposition algorithm, and the improved decomposition algorithms become more and more complicated, and some new problems such as the decomposed component is not model based also emerge. In this paper, we try to solve the two problems through another way. We think they are caused not by the insufficient decomposition algorithm but by the dogmatic input. Freeman–Durden decomposition explicitly assumes reflection symmetry. Its input is a direct truncation of the measured coherency matrix. The truncation can be regarded as a reflection symmetry approximation (RSA) of the measured coherency matrix. We first show some reasons why we do not think the truncation is a good RSA. Then, a new RSA is proposed based on the sum of three reflection symmetry components derived from the measured coherency matrix. Experimental results with several real POLSAR images show that, if the new RSA is used as the input of Freeman–Durden decomposition, the above-mentioned two problems no longer exist.
关键词： Polarimetric Synthetic Aperture Radar (POLSAR),radar polarimetry,Polarimetric decomposition
更新于2025-09-23 15:22:29
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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
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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
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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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Ship Detection Based on Deep Convolutional Neural Networks for Polsar Images
摘要： In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
关键词： Deep convolutional neural networks,polarimetric synthetic aperture radar (PolSAR),ship detection
更新于2025-09-23 15:21:21
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Saliency detection of targets in polarimetric SAR images based on globally weighted perturbation filters
摘要： In this paper, a saliency detection for Polarimetric Synthetic Aperture Radar (PolSAR) images is proposed based on weighted perturbation filters. Auxiliary data is demanded to identify polarimetric vector of targets, for a canonical perturbation filter. Only if the target signature was available and accurate, it would be satisfiable to apply the filter in practice. Besides, not every target can usually be detected by an individual filter, because of variant polarimetric characteristics of targets with respect to different aspects or shapes. To overcome these drawbacks, several perturbation filters are combined in the proposed method. By initializing with different parameters, these filters decompose PolSAR data into their index maps. Then, aiming to find out filters of interest, i.e., ones related to target pixels, we assume that targets to detect are sparse in PolSAR image. Thus, saliency weights are assigned to the filters, based on Jaccard distances of their index maps. Therein, the spatial sparseness between objects and their surrounding derives high weights for corresponding filters. And then, after globally fusion of refined filtering responses with the weights, saliency map is generated for every local pattern in PolSAR image. Finally, the target regions are extracted from this map, by thresholding and morphological operation. Experiments performed on real and simulated PolSAR data verify the performance of this method, in comparison with several common PolSAR detectors. Also, the proposed method finds out most targets in ground truth, without auxiliary polarimetric information provided.
关键词： Geometrical perturbation filter,Sparse spatial correlation,Polarimetric synthetic aperture radar,Saliency detection
更新于2025-09-23 15:21:01
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Multi-time scale coordinated scheduling for the combined system of wind power, photovoltaic, thermal generator, hydro pumped storage and batteries
摘要： The phenomenon of soil salinization in semi-arid regions is getting amplified and accentuated by both anthropogenic practices and climate change. Land salinization mapping and monitoring using conventional strategies are insufficient and difficult. Our work aims to study the potential of synthetic aperture radar (SAR) for mapping and monitoring of the spatio-temporal dynamics of soil salinity using interferometry. Our contribution in this paper consists of a statistical relationship that we establish between field salinity measurement and InSAR coherence based on an empirical analysis. For experimental validation, two sites were selected: 1) the region of Mahdia (central Tunisia) and 2) the plain of Tadla (central Morocco). Both sites underwent three ground campaigns simultaneously with three Radarsat-2 SAR image acquisitions. The results show that it is possible to estimate the temporal change in soil electrical conductivity (EC) from SAR images through the InSAR technique. It has been shown that the radar signal is more sensitive to soil salinity in HH polarization using a small incidence angle. However, for the HV polarization, a large angle of incidence is more suitable. This is, under considering the minimal influence of roughness and moisture surfaces, for a given InSAR coherence.
关键词： interferometric synthetic aperture radar (InSAR) coherence,polarimetric synthetic aperture radar (SAR),soil salinity,Electrical conductivity (EC)
更新于2025-09-23 15:19:57
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Complex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach
摘要： Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL has been demonstrated to be effective at dealing with complex data while providing classifier performance improvement. In this paper, a novel support vector machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC) is proposed. The proposed method leverages the easiest samples first to obtain an initial parameter vector. Then, more complex samples are gradually incorporated to update the parameter vector iteratively. Moreover, neighborhood constraints are introduced during the training process to further improve performance. Experimental results on three real PolSAR images show that the proposed method performs well on complex scenes.
关键词： polarimetric synthetic aperture radar (PolSAR),neighborhood constraint,self-paced learning (SPL),complex scenes,Classification,support vector machine (SVM)
更新于2025-09-23 15:19:57
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M-NL: Robust NL-Means Approach for PolSAR Images Denoising
摘要： This letter proposes a new method for polarimetric synthetic aperture radar (PolSAR) denoising. More precisely, it seeks to address a new statistical approach for weights computation in nonlocal (NL) approaches. The aim is to present a simple criterion using M-estimators and to detect similar pixels in an image. A binary hypothesis test is used to select similar pixels which will be used for covariance matrix estimation together with associated weights. The method is then compared with an advanced state-of-the-art PolSAR denoising method named NL-SAR. The filter performances are measured by a set of different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, and edge preservation on a set of simulated PolSAR images. Finally, results for RADARSAT-2 PolSAR data are presented.
关键词： M-estimators,nonlocal (NL) means,Wishart distribution,polarimetric synthetic aperture radar (PolSAR),Detection
更新于2025-09-19 17:15:36