ACR-Net: Adaptive Correlation Refined Hyperspectral Unmixing

Hyperspectral unmixing aims to resolve the prevalent issue of mixed pixels in hyperspectral imagery and serves as an effective technique for sub-pixel level image interpretation. Recent years have seen the emergence of advanced unmixing algorithms that integrate both spatial and spectral information. However, existing methods mainly focus on spatial context and lack depth in modeling spatial correlations. Both relevant and irrelevant spatial information is introduced into the spectral mixing model for local pixels, with the irrelevant information acting as noise that impacts the unmixing accuracy. To address these challenges, we propose an advanced spectral mixing model, Adaptive Correlation Refined Network (ACR-Net) which integrates refined spatial correlation based on self-attention. A key component of ACR-Net is the Adaptive Correlation Aggregated Decoder (ACAD), which extracts affinity information from the encoder’s feature map and adaptively amplifies the influence of highly correlated regions in the unmixing process. Additionally, the Composite Active Spatial Attention (CASA) Module emphasizes unique spectral characteristics across bands, improving spatial distribution representation and enabling more accurate abundance estimation. We conducted abundant evaluations on six datasets, including three real-world and two synthetic hyperspectral unmixing datasets as well as a large benchmark classification dataset. Extensive experiments have demonstrated that the proposed algorithm can achieve exceptional or comparable unmixing results among various state-of-the-art algorithms. Moreover, the proposed method achieved optimal classification performance on the classic PaviaU dataset, indicating its strong potential for hyperspectral classification task.