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In this letter, a low-rank and sparse representation classifier with a spectral consistency constraint (LRSRC-SCC) is proposed. Different from the SRC that represents samples individually, LRSRC-SCC reconstructs samples jointly and is able to capture the local and global structures simultaneously. In this proposed classifier, an adaptive spectral constraint is imposed on both the low-rank and sparse...
By considering the cubic nature of hyperspectral image (HSI) and to address the issue of the curse of dimensionality, we introduce a tensor locality preserving projection (TLPP) algorithm for HSI classification. TLPP has been proved to be effective in preserving the geometrical structure of data for dimensionality reduction. More importantly, data can be taken directly in the form of a tensor of arbitrary...
This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be...
In this paper, a novel nonlocal dictionary learning method is proposed for sparse-representation-based classification (SRC) to label high-dimensional hyperspectral imagery (HSI). In SRC, the conventional dictionary is constructed using all of the training pixels, which is inefficient due to the high-dimension low-sample-size classification problem. In this paper, we construct the dictionary by adding...
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