Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization

Rongchao Yang, Beilei Fan, Ren Wei, Yuting Wang and Qingbo Zhou
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Rongchao Yang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Beilei Fan: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ren Wei: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Yuting Wang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Qingbo Zhou: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Land, 2022, vol. 11, issue 2, 1-12

Abstract: Precise and timely classification of land cover types plays an important role in land resources planning and management. In this paper, nine kinds of land cover types in the acquired hyperspectral scene are classified based on the kernel collaborative representation method. To reduce the spectral shift caused by adjacency effect when mining the spatial-spectral features, a correlation coefficient-weighted spatial filtering operation is proposed in this paper. Additionally, by introducing this operation into the kernel collaborative representation method with Tikhonov regularization (KCRT) and discriminative KCRT (DKCRT) method, respectively, the weighted spatial-spectral KCRT (WSSKCRT) and weighted spatial-spectral DKCRT (WSSDKCRT) methods are constructed for land cover classification. Furthermore, aiming at the problem of difficulty of pixel labeling in hyperspectral images, this paper attempts to establish an effective land cover classification model in the case of small-size labeled samples. The proposed WSSKCRT and WSSDKCRT methods are compared with four methods, i.e., KCRT, DKCRT, KCRT with composite kernel (KCRT-CK), and joint DKCRT (JDKCRT). The experimental results show that the proposed WSSKCRT method achieves the best classification performance, and WSSKCRT and WSSDKCRT outperform KCRT-CK and JDKCRT, respectively, obtaining the OA over 94% with only 540 labeled training samples, which indicates that the proposed weighted spatial filtering operation can effectively alleviate the spectral shift caused by adjacency effect, and it can effectively classify land cover types under the situation of small-size labeled samples.

Keywords: land cover classification; hyperspectral images; kernel collaborative representation; weighted spatial-spectral features (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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