Fusion of Hyperspectral and Panchromatic Images Using Generative Adversarial Network and Image Segmentation

Abstract

Hyperspectral (HS) image fusion aims at integrating a panchromatic (PAN) image and an HS image, featuring the fused image with the spatial quality of the former and the spectral diversity of the latter. The classic fusion algorithm generally includes three consecutive procedures that are upsampling, detail extraction, and detail injection. In this article, we propose an HS and PAN image fusion method based on generative adversarial network and local estimation of injection gain. Instead of upsampling the HS image by classical interpolation techniques, a generative adversarial super-resolution network (GASN) is designed to obtain the interpolated HS image in the fusion framework. GASN establishes a spectral-information-based discriminator to conduct adversarial learning with the generator, so as to preserve the spectral information of the low-resolution HS image. An image segmentation-based injection gain estimation (ISGE) algorithm is subsequently proposed for HS and PAN images fusion. The injection gain is estimated over image segments obtained by a binary partition tree approach to improve the fusion performance. The proposed GASN and ISGE are implemented into two credible global estimation pansharpening methods, and experimental results prove the performance improvement of the proposed method. The proposed method is also compared with existing state-of-the-art methods, and experiments on several public databases demonstrate that the proposed method is competitive or superior to the state-of-the-art fusion methods.