Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Yang, Fan; Gao, Wei; Xu, Bin; Yang, Jian

doi:10.3390/rs70708469

Cited by 22 publications

(22 citation statements)

References 34 publications

(47 reference statements)

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“…The underlying datasets were acquired with high temporal and spatial correlation (see Table 1, Figure 1). One common method to classify a generic SAR image (not only for sea ice) based on polarimetric features is the unsupervised Wishart classification ( [41,42]), a more recent method ( [43]) uses Riemannian manifold classification, that does not rely on particular parametric assumptions on the distributions. In addition to quad polarimetric SAR, compact polarimetry has been found to offer a good compromise between the number of polarization channels and coverage (see [44]).…”

Section: Introductionmentioning

confidence: 99%

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Ressel

Singha

2016

Remote Sensing

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This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types.

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Section: Introductionmentioning

confidence: 99%

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Ressel

Singha

2016

Remote Sensing

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“…Moreover, Wishart classifier (middle part of Figure 1) was also employed as an independent method to assess the efficiency of the proposed procedure. This classifier has been widely used as a baseline for performance evaluation of the developed algorithms for PolSAR data classification in many researches (Haddadi et al, 2011;Maghsoudi et al, 2012;Salehi et al, 2014;Yang, Gao, Xu, & Yang, 2015).…”

Section: Methodsmentioning

confidence: 99%

A class-based approach to classify PolSAR imagery using optimum classifier

Safaee

Sahebi

2019

European Journal of Remote Sensing

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Classification of Polarimetric SAR (PolSAR) imagery is still one of the challenges in active remote sensing applications. Although a large number of features and different classifiers have been proposed, no unique approach has been found to satisfy all of the images and classes yet. In this paper, given the extracted features from PolSAR data, a new class-based feature selection (CBFS) algorithm is proposed to find the most optimum features for each class. Maximizing the discrimination of each class from the others is the main contribution of the CBFS which yields distinctive features. The selected features are then employed by classifiers to generate different classification results. Finally, a new approach is developed to combine these classification results to produce the final land cover map. Five different classifiers of Wishart Maximum Likelihood, Gaussian Maximum Likelihood, Support Vector Machine, Multi Layer Perceptron and Fuzzy Inference System are also used for classification. Given the CBFS results, two different Radarsat-2 and AirSAR PolSAR data were classified. Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods. Moreover, Comparison results demonstrate that the fuzzy classifiers could improve the accuracies about 3% if they are suitably constructed and well designed. The achieved higher overall accuracy for the final classified map shows the effectiveness of the proposed approach over the other compared classification procedures.

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“…The resurgent development of many theoretical analysis frameworks [22,23] and effective algorithms [24] has been witnessed. The applications of the sparse signal representation technique mainly include radar imaging [25,26], image restoration [27], image classification [28,29], and pattern recognition [15,30]. The key of sparse signal model is based on the fact that a certain signal can be represented by an overcomplete basis set (dictionary).…”

Section: Srcmentioning

confidence: 99%

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

et al. 2018

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Abstract:The monogenic signal, which is defined as a linear combination of a signal and its Riesz-transformed one, provides a great opportunity for synthetic aperture radar (SAR) image recognition. However, the incredibly large number of components at different scales may result in too much of a burden for onboard computation. There is great information redundancy in monogenic signals because components at some scales are less discriminative or even have negative impact on classification. In addition, the heterogeneity of the three types of components will lower the quality of decision-making. To solve the problems above, a scale selection method, based on a weighted multi-task joint sparse representation, is proposed. A scale selection model is designed and the Fisher score is presented to measure the discriminative ability of components at each scale. The components with high Fisher scores are concatenated to three component-specific features, and an overcomplete dictionary is built. Meanwhile, the scale selection model produces the weight vector. The three component-specific features are then fed into a multi-task joint sparse representation classification framework. The final decision is made in terms of accumulated weighted reconstruction error. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset have proved the effectiveness and superiority of our method.

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Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Cited by 22 publications

References 34 publications

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

A class-based approach to classify PolSAR imagery using optimum classifier

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

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