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Article Dans Une Revue IEEE Geoscience and Remote Sensing Letters Année : 2020

Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification

Bo Ren
Biao Hou
Licheng Jiao

Résumé

This letter proposes a novel method for combining multiview features in polarimetric synthetic aperture radar (PolSAR) for land cover classification. It is well-known that feature extraction and classifier design are two significant steps in machine learning methods for PolSAR data interpretation. Each PolSAR pixel can be represented in different feature spaces, such as polarimetric data scattering, or the polarimetric target decomposition spaces. In this letter, a tensor-based multiview embedding algorithm is proposed to fuse those features from different spaces in order to obtain a distinctive set of features for the subsequent classification. Based on the pixel-based classification tasks, a modified tensor distance (MTD) is designed to accurately calculate the distance between tensors. It emphasizes the importance of the central pixel, and decreases the influence of the neighbors in the feature patch when calculating tensor distance. Furthermore, the complementary properties of different views are exploited by an MTD measured tensor multiview spectral embedding method, so as to obtain relevant low-dimensional features. Compared with state-of-the-art methods, the validation and effectiveness of the proposed method is demonstrated on two real PolSAR data sets.
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Dates et versions

hal-03142177 , version 1 (15-02-2021)

Identifiants

Citer

Bo Ren, Biao Hou, Jocelyn Chanussot, Licheng Jiao. Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification. IEEE Geoscience and Remote Sensing Letters, 2020, 17 (12), pp.2095-2099. ⟨10.1109/LGRS.2019.2962185⟩. ⟨hal-03142177⟩
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