Abstract

Although hyperspectral data are becoming increasingly popular, they are difficult to use effectively due to the significant redundancy of such data. This article discusses a number of general-purpose dimensionality reduction techniques as a counter-redundancy measure that can be used in conjunction with known spectral dissimilarity measures. The Euclidean distance, spectral angle, and divergence of spectral information are used as such dissimilarity measures. In order to map into a space of reduced dimension, we use nonlinear mapping (NLM), isomap, locally linear embedding (LLE), Laplacian eigenmaps, and uniform manifold approximation and projection (UMAP). Quality assessment is performed using well-known hyperspectral scenes based on the results obtained using the nearest neighbor (NN) classifier and support vector machine.

中文翻译：

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高光谱图像光谱相异度测量和降维技术的比较

摘要

尽管高光谱数据正变得越来越流行，但由于此类数据的显着冗余，它们难以有效使用。本文讨论了许多通用降维技术，作为一种反冗余措施，可以与已知的频谱相异措施结合使用。欧几里得距离、光谱角度和光谱信息的发散度被用作这种相异性度量。为了映射到降维空间，我们使用非线性映射 (NLM)、等值映射、局部线性嵌入 (LLE)、拉普拉斯特征映射和均匀流形近似和投影 (UMAP)。基于使用最近邻 (NN) 分类器和支持向量机获得的结果，使用众所周知的高光谱场景进行质量评估。