Noise-adjusted subspace linear discriminant analysis for hyperspectral-image classification
Noise-adjusted subspace linear discriminant analysis for hyperspectral-image classification
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DOI：
10.1109/whispers.2012.6874295
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Wei Li;S. Prasad;J. Fowler;Q. Du
中科院分区：
文献类型：
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作者：
Wei Li;S. Prasad;J. Fowler;Q. Du
当应用于线性判别分析时，解决小样本量问题的传统解决方案是在主成分子空间中实现后者，这种策略称为子空间线性判别分析。在这项工作中，通过耦合子空间线性判别分析和噪声调整主成分分析来扩展该方法，以提供高维数据的抗噪声特征提取和分类。使用高光谱图像评估所得到的噪声调整子空间线性判别分析，实验结果表明，与传统的基于子空间的线性判别方法相比，所提出的方法不仅提供了优越的分类性能，而且即使在存在噪声的情况下也能有效地降低分类维度。
The traditional solution to addressing the small-sample-size problem as it applies to linear discriminant analysis is to implement the latter in a principal-component subspace, a strategy known as subspace linear discriminant analysis. In this work, this approach is extended by coupling subspace linear discriminant analysis and noise-adjusted principal component analysis in order to provide noise-robust feature extraction and classification of high-dimensional data. The resulting noise-adjusted subspace linear discriminant analysis is evaluated using hyperspectral imagery, with experimental results demonstrating that the proposed approach provides not only superior classification performance as compared to traditional subspace-based linear-discriminant methods but also effective dimensionality reduction for classification even in the presence of noise.