Hyperspectral Image Classification via Kernel Sparse Representation
Hyperspectral Image Classification via Kernel Sparse Representation
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DOI：
10.1109/tgrs.2012.2201730
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发表时间：
2013-01-01
影响因子：
8.2
通讯作者：
Tran, Trac D.
中科院分区：
文献类型：
--
作者：
Chen, Yi;Nasrabadi, Nasser M.;Tran, Trac D.
提出了一种新的非线性高光谱图像分类方法。我们的方法依赖于稀疏表示测试样本的所有训练样本在一个核函数引起的特征空间。对于特征空间中的每个测试像素，通过使用基于核的贪婪追求算法在相同的特征空间中通过在训练字典上分解测试像素来获得稀疏表示向量。然后直接使用恢复的稀疏表示向量来确定测试像素的类别标签。将样本投影到高维特征空间并将稀疏表示核化，提高了不同类别之间的数据可分性，与更传统的基于线性稀疏的分类算法相比，提供了更高的分类精度。此外，还通过核化联合稀疏模型将相邻像素之间的空间相干性合并，其中通过选择几个公共训练样本来在特征空间中联合表示小邻域内的所有像素。本文提出了核贪婪优化算法来解决单像素和多像素联合稀疏恢复问题的核版本。实验结果表明，该技术优于线性稀疏分类技术，以及经典的支持向量机和稀疏核逻辑回归分类器。
In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.