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For hyperspectral data classification, feature reduction techniques have become an apparent need to extract information from original data. In this paper, we introduce Locality Sensitive Discriminant Analysis (LSDA) to perform feature reduction for classification of hyperspectral imagery. By preserving both the discriminant and local geometrical structure in the data, the proposed method can obtain...
Stable local feature detection and representation are the fundamental components of target recognition and image retrieval. The traditional SIFT algorithm's descriptor of the feature points is a 128-element vector, and a lot of redundant information is presence. So the brief and effective expression of the image feature information is the key to improve the performance of the algorithm. This paper...
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