Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing
This addresses the problem of preserving both global and local information in remote sensing image analysis for researchers in hyperspectral unmixing, representing a hybrid incremental improvement.
The paper tackles hyperspectral unmixing by proposing T-CAGU, a framework that combines transformers for global dependencies and content-adaptive graph neural networks for local relationships, achieving state-of-the-art results in experiments.
Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundances. Despite significant progress in this field using deep learning, most methods fail to simultaneously characterize global dependencies and local consistency, making it difficult to preserve both long-range interactions and boundary details. This letter proposes a novel transformer-guided content-adaptive graph unmixing framework (T-CAGU), which overcomes these challenges by employing a transformer to capture global dependencies and introducing a content-adaptive graph neural network to enhance local relationships. Unlike previous work, T-CAGU integrates multiple propagation orders to dynamically learn the graph structure, ensuring robustness against noise. Furthermore, T-CAGU leverages a graph residual mechanism to preserve global information and stabilize training. Experimental results demonstrate its superiority over the state-of-the-art methods. Our code is available at https://github.com/xianchaoxiu/T-CAGU.