Hyperspectral anomaly detection (HAD) is one of the momentous technologies in the field of Earth observation and remote sensing monitoring. Profiting from puissant deep feature extraction abilities, deep convolutional networks (DCN) perform excellently in the HAD domain. Nevertheless, limited by the restriction of unique local receptive fields, DCN-based detection methods struggle to catch the long-range dependence from a global perspective. In contrast, vision transformers (ViTs) perform better in global feature extraction but still disregard the local dependence properties. To this end, we proposed a novel method entitled the global-to-local spatial-spectral awareness transformer (G2LSSAT) network, in which the global transformer block (GTB) and local transformer block (LTB) are deployed in sequence to capture deep reconstruction characteristics from the global view to the local view in a spatial-spectral domain. In particular, the GTB is designed to explore the global spatial-spectral characteristics that are dependent on a crossbar-based global sparse attention module. Furthermore, the global glanced image is divided into multiple local patches and the LTB is devised to learn the local spatial-spectral features supported by a patch-based local self-invisible attention module. In addition, considering that the abnormal pixels always be unexpectedly reconstructed with the conventional self-attention module in ViTs, we introduce a invisible diagonal mask (IDM), which is embedded into the LTB module, to overshadow each pixel itself in the receptive field and reconstruct itself based on global and local dependent spatial-spectral features. Extensive experimental results on six datasets illustrate the superiority of the proposed G2LSSAT compared with other state-of-the-art detectors.

中文翻译：

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用于高光谱异常检测的全局到局部空间光谱感知变压器网络


高光谱异常检测（HAD）是对地观测和遥感监测领域的重要技术之一。得益于强大的深度特征提取能力，深度卷积网络 (DCN) 在 HAD 领域表现出色。然而，受限于独特的局部感受野的限制，基于 DCN 的检测方法很难从全局角度捕获长程依赖性。相比之下，视觉变换器（ViT）在全局特征提取方面表现更好，但仍然忽略局部依赖性特性。为此，我们提出了一种名为全局到局部空间频谱感知变换器（G2LSSAT）网络的新方法，其中全局变换器块（GTB）和局部变换器块（LTB）按顺序部署以捕获深度重建空间谱域中从全局视图到局部视图的特征。特别是，GTB 旨在探索依赖于基于 crossbar 的全局稀疏注意力模块的全局空间光谱特征。此外，全局扫视图像被分为多个局部补丁，LTB 被设计为学习由基于补丁的局部自不可见注意模块支持的局部空间光谱特征。此外，考虑到ViTs中传统的自注意力模块总是会意外地重建异常像素，我们引入了一种嵌入到LTB模块中的不可见对角掩模（IDM），以遮盖感受野中的每个像素本身，基于全局和局部相关的空间光谱特征重建自身。 六个数据集上的大量实验结果说明了所提出的 G2LSSAT 与其他最先进的检测器相比的优越性。