关键词: 降维方法, 信息熵, 高光谱遥感影像

Abstract: Hyperspectral remote sensing images provide almost continuous spectrum for the surface observation of the earth. However, there are more redundancy information within the bands, which pose challenges to storage, analysis and visualization of the hyperspectral remote sensing images. Thus, dimensionality reduction for hyperspectral remote sensing images become a key step of preprocessing. In this paper, the theory of information entropy is adopted to design the dimensionality reduction method. Each band of hyperspectral remote sensing images are abstracted as independent individual, and the decision table matrix of hyperspectral remote sensing images are designed, and then the bands are ordered based on the information gain. According to the accuracy requirement of the analysis of hyperspectral remote sensing images, the different bands are selected based on the ordered bands, which are considered as the results of the dimensionality reduction. Taking the classification accuracy of remote sensing as an example to evaluate the feasibility and superiority of the dimensionality reduction methods in hyperspectral remote sensing, and based on the experimental results, the proposed method has marked advantages and outperformed other methods.

Key words: dimensionality reduction method, information entropy, hyperspectral remote sensing image