An Unmixing-Based Multi-Attention GAN for Unsupervised Hyperspectral and Multispectral Image Fusion
Lijuan Su, Yuxiao Sui, Yan Yuan
Abstract
Hyperspectral images (HSI) frequently have inadequate spatial resolution, which hinders numerous applications for the images. High resolution multispectral image (MSI) has been fused with HSI to reconstruct images with both high spatial and high spectral resolutions. In this paper, we propose a generative adversarial network (GAN)-based unsupervised HSI-MSI fusion network. In the generator, two coupled autoencoder nets decompose HSI and MSI into endmembers and abundances for fusing high resolution HSI through the linear mixing model. The two autoencoder nets are connected by a degradation-generation (DG) block, which further improves the accuracy of the reconstruction. Additionally, a coordinate multi-attention net (CMAN) is designed to extract more detailed features from the input. Driven by the joint loss function, the proposed method is straightforward and easy to execute in an end-to-end training manner. The experimental results demonstrate that the proposed strategy outperforms the state-of-art methods.