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• W2917503437 abstract "ABSTRACTHyperspectral image (HSI) classification has been a vibrant area of research in recent years. In this letter, a salient feature extraction method is designed to improve the HSI classification performance. First, three consecutive bands of HSI are decomposed into several elements in order to better extract salient feature. Second, two measures of contrast that rate the uniqueness and the spatial distribution of these elements are computed. Thirdly, two measures of contrast are combined to form the saliency value of an element. The final saliency of a pixel is formed by a weighted linear combination of its surrounding elements saliency. Finally, three band sliding window is used over the spectral dimension of HSI and salient features extracted from each three adjacent bands are stacked to form the final features. The important characteristic of the proposed approach is that both local spatial information and global spatial information are taken into account in the process of feature extraction. Expe..." @default.
• W2917503437 created "2019-03-02" @default.
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• W2917503437 date "2019-02-22" @default.
• W2917503437 modified "2023-09-29" @default.
• W2917503437 title "Salient feature extraction for hyperspectral image classification" @default.
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• W2917503437 doi "https://doi.org/10.1080/2150704x.2019.1579936" @default.
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