Saliency Analysis and Gaussian Mixture Model-Based Detail Extraction Algorithm for Hyperspectral Pansharpening

Abstract

The purpose of hyperspectral (HS) pansharpening is to improve the spatial resolution of HS images using panchromatic (PAN) images so as to obtain the pansharpened images with both high spectral diversity and high spatial resolution. The classical component substitution (CS)-based pansharpening approaches can be decomposed into two sequential phases: detail extraction and detail injection. In general, the detail extraction is performed by computing the difference between the PAN image and a weighted average of the HS bands, whereas the detail injection depends on the injection gain which is defined locally or globally. In this article, we introduce a novel pansharpening algorithm in which the extracted details and the injection gain are estimated over salient and nonsalient regions achieved via saliency analysis and Gaussian mixture model. The proposed method is applied to four credible CS-based pansharpening methods and also compared to other state-of-the-art methods. Experimental results show that the modified CS methods have better performance than the original methods. In addition, our method also achieves comparable or better performance than the other state-of-the-art methods.