Embedding Spatial Context into Spectral Angle Based Nonlinear Mapping for Hyperspectral Image Analysis

摘要

Due to the high dimensionality and redundancy of hyper-spectral images, an important step in analyzing such images is to reduce the dimensionality. In this paper, we propose and study the dimensionality reduction technique, which is based on the approximation of spectral angle mapper (SAM) measures by Euclidean distances. The key feature of the proposed method is the integration, of spatial information into the dissimilarity measure. The experiments performed on the open hyper-spectral datasets showed that the developed method can be used in the analysis of hyperspectral images.