Support Vector Machine for Multifrequency SAR Polarimetric Data Classification

Lardeux, Cédric; Frison, Patrice; Tison, C.; Souyris, J.-C.; Stoll, Benoît; Fruneau, Bénédicte; Rudant, Jean-Paul

doi:10.1109/tgrs.2009.2023908

Cited by 236 publications

(125 citation statements)

References 30 publications

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“…Some recent studies with the NN technique were presented in [11], where second-kind statistics were used as classifying features; SVMs have been used in [10,12], comparing them with a supervised Wishart classifier in the latter; in [61], RBFNNs were used for an automatic target recognition application. The K-means classifier is an unsupervised clustering approach.…”

Section: Final Resultsmentioning

confidence: 99%

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Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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Section: Final Resultsmentioning

confidence: 99%

“…Most of the studies related to SAR classification are focused on land areas, distinguishing, for instance, between urban areas, arid land, forest, water, etc. [10][11][12][13][14]. In [15], an ocean feature detection, extraction and classification scheme is presented.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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“…The SVM method is also a non-parametric approach, which does not rely on the assumption that the dataset follows a specific statistical distribution; this makes it well adapted to polarimetric SAR data, which can have different distributions depending on the studied target and the polarimetric parameter [42]. It has demonstrated its potential for land cover classification using SAR imagery [41,[43][44][45] and has been used for various types of applications, such as the classification of rice crops [46], for the delimitation and mapping of snow and sea ice [47][48][49], as well as forest vegetation classification [43,50,51].…”

Section: Class Symbolmentioning

confidence: 99%

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

et al. 2016

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Abstract:The expansion of shrub vegetation in Arctic and sub-Arctic environments observed in the past decades can have significant effects on northern ecosystems. There is a need for efficient tools to monitor those changes, not only in terms of the spatial coverage of shrubs, but also their vertical growth. The objective of the current paper is to evaluate the performance of polarimetric C-band SAR datasets for land cover classification in sub-Arctic environments. A series of RADARSAT-2 quad-pol images were acquired between October 2011 and April 2012. The Support Vector Machine (SVM) classification scheme was used on three sets of features: the elements of the polarimetric coherency matrix [T], the parameters extracted from a polarimetric decomposition based on the eigenvalues and eigenvectors of [T] and the parameters extracted from a model-based decomposition. Using a single image, the results show that the best classification accuracies (≈ 75%) are obtained using the [T] matrix with the October images. When adding a second image to the feature set, either from two different dates or two incidence angles, the classification accuracy is improved and reaches 90.1% with two images from October 2011 and April 2012 at 27 • incidence. The results show that C-band polarimetric SAR imagery is an adequate tool to map shrub vegetation in sub-Arctic environments.

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“…Since Freeman and Durden proposed the three-component decomposition in 1992 [2] and 1998 [3], more than 20 decompositions have been published [4][5][6][7][8][9][10][11][12][13][14][15][16]. Model-based decomposition has been successfully used in PolSAR image classification [17][18][19][20][21], speckle filtering [22], polarimetric SAR Interferometry [23], wetland research [24], soil moisture and roughness estimation [25][26][27], target detection [28,29], disaster assessment [30], and so on. In the past several years, the largest advances include adaptive scattering models [16,26,31,32], Orientation Angle Compensation (OAC) [33], Nonnegative Eigenvalue Constraint (NNEC) [7], etc.…”

Section: Introductionmentioning

confidence: 99%

Improved van Zyl Polarimetric Decomposition Lessening the Overestimation of Volume Scattering Power

2014

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This paper improves van Zyl's Nonnegative Eigenvalue Decomposition (NNED). Orientation angle compensation and helix scattering are introduced to the decomposition. The volume scattering parameters that explain the most cross-polarized power are selected. If volume scattering and helix scattering explain all cross-polarized power in the measured coherency matrix, then simply perform van Zyl decomposition to the remainder matrix; otherwise, the measured coherency matrix is decomposed into three components, i.e., helix scattering, volume scattering, and one ground scattering. The latter two scattering are all modeled by Neumann's adaptive depolarizing model, according to which some cross-polarized power is attributed to ground scattering hence the orientation angle randomness of volume scattering and the dominant ground scattering are obtained. In this way, all cross-polarized power could be well explained. Experiments using UAVSAR data showed that more than 99.8% of total pixels are well fitted. Negative power is avoided. Compared with van Zyl decomposition, volume scattering power is reduced by up to 8.73% on average. The given volume scattering power is often lower than that by three latest NNED.

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Support Vector Machine for Multifrequency SAR Polarimetric Data Classification

Cited by 236 publications

References 30 publications

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

Improved van Zyl Polarimetric Decomposition Lessening the Overestimation of Volume Scattering Power

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