Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
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It is of great interest in spectral-spatial features classification for hyperspectral images (HSI) with high spatial resolution. This paper presents a novel Spectral-spatial classification method for improving hyperspectral image classification accuracy. Specifically, a new texture feature extraction algorithm exploits spatial texture feature from spectrum is proposed. It employs local binary patterns (LBPs) in order to extract the image texture feature with respect to spectrum information diversity (SID) to measure the differences of spectrum information. The classifier adopted in this work is support vector machine (SVM) because of its outstanding classification performances. In this paper, two real hyperspectral image datasets are used for testing the performance of the proposed method. Our experimental results from real hyperspectral images indicate that the proposed framework can enhance the classification accuracy compare to traditional alternatives.
KeywordsHyperspectral image classification Spectral-spatial analysis Local binary patterns Spectrum information diversity Support vector machine
This work was supported in part by National Natural Science foundations of China (Grant Nos. 41301382, 61401439, 41604113, 41711530128) and foundation of Key lab of spectral imaging, Xi’an Institute of Optics and Precision Mechanics of CAS.
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