Hyperspectral image (HSI) denoising is a crucial step for its subsequent applications. In this article, we propose TDSAT, a 3-D spatial-spectral attention Transformer model designed to effectively remove noise in HSI processing while preserving essential spectral and spatial information. The primary objective of this model is to utilize the 3-D Transformer to explore the global spectral-spatial features in HSI, learn the relationships among different bands, and preserve high-quality spectral and spatial information for denoising. The proposed method consists of three main components: the multihead spectral attention (MHSA) module, the gated-dconv feedforward network (GDFN) module, and the spectral enhancement (SpeE) module. The MHSA module learns the relationships among different bands and emphasizes the local spatial information. The GDFN module explores more expressive and discriminative spectral features. The SpeE module enhances the perception of subtle differences between different spectrums. Moreover, unlike the previous Transformer denoising method that can only handle fixed bands, the proposed method combines 3-D convolution and spectral-spatial attention Transformer blocks, enabling the denoising of HSI with an arbitrary number of bands. Experimental results demonstrate that TDSAT outperforms compared methods. The code is available at https://github.com/ Featherrain/TDSAT.

中文翻译：

---

用于高光谱图像去噪的三维空间光谱注意力变换器


高光谱图像（HSI）去噪是其后续应用的关键一步。在本文中，我们提出了 TDSAT，这是一种 3D 空间光谱注意力 Transformer 模型，旨在有效去除 HSI 处理中的噪声，同时保留必要的光谱和空间信息。该模型的主要目标是利用 3-D Transformer 探索 HSI 中的全局光谱空间特征，了解不同波段之间的关系，并保留高质量的光谱和空间信息以进行去噪。该方法由三个主要组件组成：多头谱注意（MHSA）模块、门控dconv前馈网络（GDFN）模块和谱增强（SpeE）模块。 MHSA模块学习不同频段之间的关系并强调局部空间信息。 GDFN 模块探索更具表现力和辨别力的光谱特征。 SpeE 模块增强了对不同光谱之间细微差异的感知。此外，与之前只能处理固定频段的 Transformer 去噪方法不同，该方法结合了 3D 卷积和频谱空间注意力 Transformer 块，能够对任意数量的频段进行 HSI 去噪。实验结果表明 TDSAT 优于对比方法。代码可在 https://github.com/Featherrain/TDSAT 获取。