A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing

Abstract

Hyperspectral unmixing is a crucial technique in remote sensing data processing that aims to estimate component information from mixed pixels in hyperspectral images. Most existing deep learning-based hyperspectral unmixing models employ autoencoder (AE) networks to reconstruct hyperspectral images and estimate abundance maps. Here, the weight between the reconstructed and the softmax layers is used to extract/estimate endmember signatures. However, AEs are heavily dependent on initial weights, which introduces inherent randomness, potentially compromising unmixing accuracy. To address this issue, in this article, we present a new dual-feature fusion network (DFFN) for enhanced hyperspectral unmixing. Our DFFN mainly consists of four modules: 1) a feature fusion module (FFM); 2) an abundance estimation module (AEM); 3) an endmember estimation module (EEM); and 4) a reconstruction module (RM). First, FFM calculates spectral and spatial similarities and then enhances the hyperspectral image by matrix multiplications with similarity matrices. Second, AEM takes the enhanced hyperspectral image as input and uses convolutional layers to estimate abundances and reconstruct the image. Next, the reconstructed image is fed into EEM to automatically estimate endmembers. RE performs the final reconstruction through matrix multiplication of the estimated endmembers and abundances. Experiments on synthetic and real hyperspectral datasets, together with a comparison with state-of-the-art techniques, demonstrate the superiority of our newly proposed DFFN. The full code is released at https://github.com/xuanwentao for public evaluation.