Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
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DOI：
10.1109/tgrs.2011.2165957
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发表时间：
2012-04-01
影响因子：
8.2
通讯作者：
Bruce, Lori Mann
中科院分区：
文献类型：
--
作者：
Li, Wei;Prasad, Saurabh;Bruce, Lori Mann
高光谱图像通常为图像中的每个像素提供在广泛的电磁频谱范围内捕获的丰富信息；然而，当用于统计模式分类任务时，所得到的高维特征空间往往会导致病态公式。流行的降维技术，如主成分分析、线性判别分析及其变体，通常假设高斯分布。通常用于高光谱分析的二次最大似然分类器也假设单高斯类条件分布。从这种单高斯假设出发，提出了一种旨在利用数据丰富统计结构的分类范式。该框架采用局部Fisher判别分析来降低数据的维数，同时保留其多模态结构，而随后的高斯混合模型或支持向量机则对降维后的多模态数据进行有效分类。在几种不同的多类别高光谱分类任务上的实验结果表明，该方法明显优于几种传统的方法。
Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality-reduction techniques such as principal component analysis, linear discriminant analysis, and their variants typically assume a Gaussian distribution. The quadratic maximum-likelihood classifier commonly employed for hyperspectral analysis also assumes single-Gaussian class-conditional distributions. Departing from this single-Gaussian assumption, a classification paradigm designed to exploit the rich statistical structure of the data is proposed. The proposed framework employs local Fisher's discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, while a subsequent Gaussian mixture model or support vector machine provides effective classification of the reduced-dimension multimodal data. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed approach significantly outperforms several traditional alternatives.