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Hyperspectral Image Classification Using Kernel Fused Representation via a Spatial-Spectral Composite Kernel With Ideal Regularization
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DOI：10.1109/LGRS.2019.2898913.png)
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Abstract
En 中文
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.
Keywords:
Composite kernel (CK)
hyperspectral image (HSI) classification
ideal regularization (IR)
Kernel fused representation
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