Abstract
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric or image features. However, it is difficult to improve the classification accuracy of PolSAR images by using all these low-level features directly, since they may conflict with each other for classification. Hence, how to joint learn these low-level features to obtain high-level discriminating features is a challenging task. To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. The proposed method extract three kinds of low-level features of PolSAR data at first. Then, a multi-feature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. Moreover, the joint sparse features are further compressed to overcome the dimension curse and acquire semantic features by the topic model. By this way, the low-level features are fused and discriminating high-level features are acquired. However, the pixel-wise feature learning method is time consuming. To speed the proposed method, a superpixel-based fast learning method is designed by involving the contextual relationship. Experiments are taken on three sets of real PolSAR data with different sensors and bands, and several compared methods are used to verify the effectiveness of the proposed method. The experimental results illustrate that the proposed method can obtain better performance than the state-of-art methods, especially for the heterogenous areas.
Highlights
Polarimetric synthetic aperture radar (PolSAR) terrain classification is the key of PolSAR image interpretation
Inspired by the sparse representation and topic model, in this paper, a novel multi-feature joint learning method is proposed for the PolSAR image classification
We propose a topic model-based multi-feature learning method for PolSAR terrain classification
Summary
Polarimetric synthetic aperture radar (PolSAR) terrain classification is the key of PolSAR image interpretation. J. Shi et al.: Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification methods, which added the scattering information to improve the classification accuracy, such as Cloude decomposition [7], Freeman decomposition [8], Huyen decomposition [9] and so forth. A multi-level feature extraction method [33] was proposed for PolSAR image classification. Inspired by the sparse representation and topic model, in this paper, a novel multi-feature joint learning method is proposed for the PolSAR image classification. Compared with the conventional classification methods, the proposed method has three characteristics as follows: 1) A joint multifeature sparse learning method is proposed to combine three types of features, which are the polarimetric data, scattering characteristics and image contextual features respectively. PROPOSED METHOD For considering the spatial relationship and semantic information, a fast multi-feature joint sparse learning method is proposed for the PolSAR image classification. Edge and line energies are computed with 3 scales and 18 orientations, and maximum energy is selected as the the contour feature for each pixel
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