Evaluation of Spectral Similarity Measures and Dimensionality Reduction Techniques for Hyperspectral Images

摘要

Hyperspectral data is becoming more and more in demand these days. However, its effective use is hindered by significant redundancy. In this paper, we analyze the effectiveness of using common dimensionality reduction methods together with known measures of spectral similarity. In particular, we use Euclidean distance, spectral angle mapper, and spectral divergence to measure dissimilarity in hyperspectral space. For the mapping to lower-dimensional space, we use nonlinear methods, namely, Nonlinear Mapping, Isomap, Locally Linear Embedding, Laplacian Eigenmaps, and UMAP. Quality assessment is performed using known hyperspectral scenes based on the results provided by the nearest neighbor classifier and support vector machine.