Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification
Kernel Collaborative Representation With Tikhonov Regularization for Hyperspectral Image Classification
复制标题
用于高光谱图像分类的核协作表示与吉洪诺夫正则化
DOI：
10.1109/lgrs.2014.2325978
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发表时间：
2015-01-01
影响因子：
4.8
通讯作者：
Xiong, Mingming
中科院分区：
文献类型：
--
作者：
Li, Wei;Du, Qian;Xiong, Mingming
在这封信中，提出了与Tikhonov正则化（KCRT）的内核协作表示，以进行高光谱图像分类。原始数据通过使用非线性映射函数来提高类可分离性，将原始数据投影到高维内核空间中。此外，邻近位置的空间信息已包含在内核空间中。对两个高光谱数据的实验结果证明，我们提出的技术的表现优于传统的支持向量机，其复合核和其他最先进的分类器，例如内核稀疏表示分类器和内核协作表示分类器。
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.