Transformers have shown significant success in hyperspectral unmixing (HU). However, challenges remain. While multi-scale and long-range spatial correlations are essential in unmixing tasks, current Transformer-based unmixing networks, built on Vision Transformer (ViT) or Swin-Transformer, struggle to capture them effectively. Additionally, current Transformer-based unmixing networks rely on the linear mixing model, which lacks the flexibility to accommodate scenarios where nonlinear effects are significant. To address these limitations, we propose a multi-scale Dilated Transformer-based unmixing network for nonlinear HU (DTU-Net). The encoder employs two branches. The first one performs multi-scale spatial feature extraction using Multi-Scale Dilated Attention (MSDA) in the Dilated Transformer, which varies dilation rates across attention heads to capture long-range and multi-scale spatial correlations. The second one performs spectral feature extraction utilizing 3D-CNNs with channel attention. The outputs from both branches are then fused to integrate multi-scale spatial and spectral information, which is subsequently transformed to estimate the abundances. The decoder is designed to accommodate both linear and nonlinear mixing scenarios. Its interpretability is enhanced by explicitly modeling the relationships between endmembers, abundances, and nonlinear coefficients in accordance with the polynomial post-nonlinear mixing model (PPNMM). Experiments on synthetic and real datasets validate the effectiveness of the proposed DTU-Net compared to PPNMM-derived methods and several advanced unmixing networks.

翻译：Transformer在高光谱解混任务中已展现出显著成效，但仍面临挑战。尽管多尺度与长程空间相关性对解混至关重要，但当前基于Vision Transformer或Swin-Transformer构建的解混网络难以有效捕捉这些特征。此外，现有基于Transformer的解混网络依赖线性混合模型，缺乏应对非线性效应显著场景的灵活性。为突破这些局限，本文提出一种面向非线性高光谱解混的多尺度空洞Transformer网络。编码器采用双分支结构：第一分支通过空洞Transformer中的多尺度空洞注意力机制，利用不同注意力头中的可变空洞率捕获长程及多尺度空间相关性；第二分支采用结合通道注意力的3D-CNN进行光谱特征提取。双分支输出经融合实现多尺度空间与光谱信息整合，进而转换为丰度估计。解码器设计兼容线性和非线性混合场景，通过依据多项式后非线性混合模型显式建模端元、丰度与非线性系数间的关系，增强了模型可解释性。在合成与真实数据集上的实验表明，相较于基于多项式后非线性混合模型的衍生方法及多种先进解混网络，所提DTU-Net具有显著优势。