Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 11, Issue 4, Pages 1118-1130Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2017.2757475
Keywords
Classification; class-oriented strategy; hyperspectral image (HSI); kernel; local structure information; sparse representation
Categories
Funding
- Natural Science Foundation of China [41471275]
- Key Scientific Instrument Development Foundation of China [2012YQ05025004]
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As a nonlinear extension of traditional sparse representation-based classifier (SRC), kernel SRC (KSRC) has shown its excellent performance for hyperspectral image (HSI) classification, by mapping the nonlinearly separable samples into high-dimensional feature space. However, the rich locality structure of HSI contains more discriminative information, which should be considered in KSRC. We intend to incorporate the locality structure and kernelmethod into a unified SR-based framework by a local spatial kernel. As a powerful texture descriptor, local binary patterns (LBP) was used to extract local feature for remote sensing. Region-level kernels are applied to calculate the distance between two LBP histogram features. To discover nonlinear similarity information between test and training samples, we integrate the LBP feature into spatial region-level kernel for HSI classification. Then, we propose a weighted kernel sparse representation classifier optimized via class-oriented strategy, which combines local structure information and SRC in the kernel feature space based on spatial region-level kernel. Experimental results on three open HSIs demonstrate that the proposed method achieves better classification performance than other state-of-the-art classification methods.
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