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(Journal Article) |
IEEE Geoscience and Remote Sensing Letters 16 (9): 1422-1426 (2019)
Hyperspectral Image Classification Using Kernel Fused Representation via a Spatial-Spectral Composite Kernel With Ideal Regularization
G Liu
,
L Qi
,
Y Tie
,
L Ma
ABSTRACT
To adequately exploit spectral, spatial, and label information of the given hyperspectral data, a kernel fused representation-based classifier via a spatial-spectral composite kernel with ideal regularization (CKIR) method is proposed in this letter. Specifically, the learned CKIR is embedded into the kernel version of representation-based classifiers, i.e., kernel sparse representation-based classifier (KSRC) and kernel collaborative representation-based classifier (KCRC), to obtain more discriminative representation coefficients. Furthermore, to benefit from both sparsity and data correlation in representation, KSRC and KCRC are combined in the CKIR-based residual domain to further enhance the discriminative ability of the proposed classifier. The experimental results on two real hyperspectral images demonstrate that the proposed method outperforms the other state-of-the-art classifiers.