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Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2014.2325978.png)
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摘要
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In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a high-dimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier.
Keyword:
Hyperspectral classification
kernel methods
nearest regularized subspace (NRS)
sparse representation
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Feature selection and classification of hyperspectral images, with support vector machines基于支持向量机的高光谱图像特征选择与分类
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