Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery
Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery
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DOI：
10.1109/tgrs.2013.2277251
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发表时间：
2014-07-01
影响因子：
8.2
通讯作者：
Fowler, James E.
中科院分区：
文献类型：
--
作者：
Ly, Nam Hoai;Du, Qian;Fowler, James E.
研究了高光谱图像的稀疏保持图构造降维方法。特别地，当标记样本可用时，提出了基于稀疏图的鉴别分析。通过迫使投影沿着样本与最好地表示它的类内样本聚类的方向，可以增强辨别能力。该方法不像传统的线性判别分析那样对标记样本数有要求，并且可以通过一个简单的广义特征值问题来求解。降维的质量进行评估的支持向量机与复合空间光谱内核。实验结果表明，提出的基于稀疏图的判别分析可以产生上级分类性能与低得多的维数相比，对原始数据或与其他降维方法转换的数据的性能。
Sparsity-preserving graph construction is investigated for the dimensionality reduction of hyperspectral imagery. In particular, a sparse graph-based discriminant analysis is proposed when labeled samples are available. By forcing the projection to be along the direction where a sample is clustered with within-class samples that best represented it, the discriminative power can be enhanced. The proposed method has no requirement on the number of labeled samples as in traditional linear discriminant analysis, and it can be solved by a simple generalized eigen-problem. The quality of the dimensionality reduction is evaluated by a support vector machine with a composite spatial-spectral kernel. Experimental results demonstrate that the proposed sparse graph-based discriminant analysis can yield superior classification performance with much lower dimensionality as compared to performance on the original data or on data transformed with other dimensionality-reduction approaches.