Kernel weighted joint collaborative representation for hyperspectral image classification

Qian Du; Wei Li

doi:10.1117/12.2179914

21 May 2015 Kernel weighted joint collaborative representation for hyperspectral image classification

Qian Du, Wei Li

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 95010V (2015) https://doi.org/10.1117/12.2179914
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Collaborative representation classifier (CRC) has been applied to hyperspectral image classification, which intends to use all the atoms in a dictionary to represent a testing pixel for label assignment. However, some atoms that are very dissimilar to the testing pixel should not participate in the representation, or their contribution should be very little. The regularized version of CRC imposes strong penalty to prevent dissimilar atoms with having large representation coefficients. To utilize spatial information, the weighted sum of local spatial neighbors is considered as a joint spatial-spectral feature, which is actually for regularized CRC-based classification. This paper proposes its kernel version to further improve classification accuracy, which can be higher than those from the traditional support vector machine with composite kernel and the kernel version of sparse representation classifier.

Citation Download Citation

Qian Du and Wei Li "Kernel weighted joint collaborative representation for hyperspectral image classification", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010V (21 May 2015); https://doi.org/10.1117/12.2179914

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