Dual-stream autoencoder for channel-level multi-scale feature extraction in hyperspectral unmixing

High-dimensional hyperspectral imagery presents significant challenges for accurate unmixing due to spectral variability, limited spatial resolution, and noise. Traditional unmixing approaches often rely on spatial multi-scale processing, leading to redundant computations and suboptimal feature representations. In response to these challenges, we propose a novel Channel Multi-Scale Dual-Stream Autoencoder (CMSDAE) that innovatively integrates channel-level multi-scale feature extraction with dedicated spectral information guidance. By leveraging Channel-level Multi-Scale Perception Blocks and a Hybrid Attention-Aware Feature Block, CMSDAE efficiently captures diverse and robust spectral-spatial features while significantly reducing computational redundancy. Extensive experiments on both synthetic and real-world datasets demonstrate that CMSDAE not only improves unmixing accuracy and robustness against noise but also offers enhanced computational efficiency compared to state-of-the-art methods. This work provides new insights into spectral-spatial modeling for hyperspectral unmixing, promising more reliable and scalable analysis in challenging remote sensing applications.