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In this letter, a sparse representation-based nearest neighbor (SRNN) classifier is proposed. Unlike the traditional $k$-nearest neighbor (NN) classifier that employs the Euclidean distance as similarity metric, the proposed SRNN considers sparse coefficients to determine the label of testing samples, since sparse coefficients can reflect the similarity between data and provide more discriminative...
In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept — a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ1-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ1-norm penalty, named...
Traditional hyperspectral image classification typically uses raw spectral signatures or simple spatial characteristics such as textural features without considering the correlation between spectral and spatial information. In this paper, we propose a spectral-spatial hyperspectral image classification based on a structured multi-modal statistical model. A 3D wavelet transform is employed to extract...
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