Hyperspectral image visualization with edge-preserving filtering and principal component analysis

Abstract

In this paper, an edge-preserving filtering and principal component analysis (PCA)-based visualization method is proposed for hyperspectral images, in which both global and local image information of hyperspectral images (HSIs) are taken into account in the proposed visualization framework that consists of the following major steps. First, the band number of the original image is reduced with averaging-based image fusion (AIF). Then, the edge-preserving filtering is performed on the dimension reduced image so as to decompose it into two components, i.e., the base layers which contain the large-scale boundary information, and the detail layers which contain the mid- and small-scale edges and textures. Next, the base layers are fused with the principal component analysis method, and the detail layers are fused with the weighted sum method, in which the fusion weights are determined by the transform matrix of the base layers. Finally, the fused detail layers are enhanced with histogram equalization and combined with the fused base layers to visualize the hyperspectral image. Experimental results on four real hyperspectral data sets demonstrate that the proposed approach performs the best in improving image contrast and preserving the details. In addition, compared with seven state-of-the-art visualization methods, the quantitative metrics obtained by the proposed method on four data sets have been increased by 80.45%, 69.29%, 138.61%, and 23.21% on average in terms of separability of features (SF), standard deviation (SD), average gradient (AG), and entropy (EN).