用Transformer和自适应图网络提升高光谱解混的全局与局部一致性
Transformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing
- 用Transformer捕捉全局依赖，自适应图网络增强局部关系
- 多阶传播动态学习图结构，噪声下仍保持稳定
- 适合需要高精度解混的遥感图像分析任务
高光谱解混旨在将遥感图像中每个混合像元分解为端元及其对应丰度。尽管深度学习已取得显著进展，但多数方法难以同时刻画全局依赖与局部一致性，导致长程交互与边界细节难以兼顾。本文提出一种新型Transformer引导的自适应图解混框架（T-CAGU），通过Transformer捕捉全局依赖，并引入内容自适应图神经网络强化局部关系。不同于以往工作，T-CAGU采用多阶传播机制动态学习图结构，增强抗噪能力；同时利用图残差机制保留全局信息并稳定训练。实验表明，该方法优于现有最先进方法。代码已开源：https://github.com/xianchaoxiu/T-CAGU。
原文摘要 · Abstract (English)
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.
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