MCDB-Net: Multiview Collaborative Dual-Branch Unmixing Network for Hyperspectral Images

Hyperspectral unmixing based on autoencoders is a crucial research task in remote sensing imagery. While existing deep hyperspectral unmixing networks primarily focus on spatial features, the inherently rich and continuous spectral bands in hyperspectral images harbor significant underutilized information. The intrinsic characteristics of these continuous bands can naturally enable more nuanced and effective modeling of mixed pixels. In this paper, we propose the multiview collaborative dual-branch network (MCDB-Net), designed to fully learn and exploit the complex spectral features in high-dimensional hyperspectral data, thereby enhancing the representation capabilities of these features. MCDB-Net constructs a novel multiview spectral block to strengthen the correlation between multiple views of pixel spectra. The full view spectral feature extraction module highlights important spectral features, while the local multiview spectral feature extraction module provides a detailed understanding of the interactions between multiview spectral information. The multiview abundance collaboration module collaboratively learns spectral feature information from different perspectives and dynamically adjusts the weights of abundance estimations, leading to better integration of abundance features across various views. Extensive experiments on different datasets demonstrate that MCDB-Net achieves higher continuity and robustness in unmixing results, showcasing its powerful capability in spectral feature extraction.