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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery
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Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery

机译：基于稀疏图的高光谱图像判别分析

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摘要

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 eigenproblem. 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.
机译：研究了稀疏图构造以减少高光谱图像的维数。特别地，当标记的样本可用时，提出了基于稀疏图的判别分析。通过迫使投影沿着样本与最能代表该样本的类内样本聚类的方向，可以增强判别力。与传统的线性判别分析方法相比，该方法对标记样本的数量没有要求，并且可以通过简单的广义特征问题来解决。降维的质量由带有复合空间光谱核的支持向量机进行评估。实验结果表明，与原始数据或使用其他降维方法转换后的数据相比，所提出的基于稀疏图的判别分析可提供出众的分类性能，且维数要低得多。

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