An Unsupervised Hyperspectral Image Fusion Method Based on Spectral Unmixing and Deep Learning

Abstract

Due to the limitations of various hardware conditions, in practice, only high resolution multispectral and low resolution hyperspectral images are usually captured. In order to apply hyperspectral images in various fields, better quality hyperspectral images have become a problem to be solved. In this paper, we propose an image fusion method based on spectral unmixing, which effectively combines the advantages of multispectral images and low-resolution hyperspectral images to generate high-resolution hyperspectral images. To be specific, a deep learning model based on spectral decomposition is constructed, using multiplication iterative rules based on the traditional gradient descent algorithm to get initial high-resolution abundance and define degeneration networks to describe the spatial and spectral downsampling operations. Experiments show that this method can get fusion images better quality than other methods.