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Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. When classifying hyperspectral and multispectral images with the existing algorithms, we use only the spectral information more often. This paper presents an one-class...
High dimensionality of hyperspectral data and relatively limited training samples induce the Hughes phenomenon in hyperspectral image classification. To prevent this problem and decrease the computational cost, feature extraction often acts as pre-processing. In this paper, a subspace weighting kernel method combining clustering-based grouping is proposed for feature extraction in hyperspectral imagery...
Anomaly detection is one of the most important applications for hyperspectral images. Conventional algorithm such as Reed-Xiaoli (RX) detector fails to be applied to hyperspectral images, which have high spectral dimensionality and complicated correlation between spectral bands. Therefore, effective feature extraction methods and selection rules are necessary. In this paper, comparative analyses of...
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