Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, HTD faces challenges due to limited prior knowledge and spectral variations, leading to underfitting models and unreliable performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the central pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Code is available at \url{https://github.com/shendb2022/HTD-Mamba}.

翻译：高光谱目标检测（HTD）在像素级别从复杂背景中识别感兴趣目标，对地观测具有重要作用。然而，由于先验知识有限和光谱变化，HTD面临模型欠拟合和性能不可靠的挑战。为解决这些问题，本文提出一种基于金字塔状态空间模型（SSM）的高效自监督HTD方法，命名为HTD-Mamba。该方法采用光谱对比学习，基于内在特征的相似性度量区分目标与背景。具体而言，为获取充足训练样本并利用空间上下文信息，我们提出一种空间编码的光谱增强技术，将图像块内所有相邻像素编码为中心像素的变换视图。此外，为探索全局波段相关性，我们将像素划分为连续的分组光谱嵌入，并首次将Mamba引入HTD，以线性复杂度建模光谱序列的长程依赖关系。进一步地，为缓解光谱变化并增强鲁棒表示，我们提出以金字塔SSM作为骨干网络，以捕获并融合多分辨率光谱维度的内在特征。在四个公开数据集上的大量实验表明，所提方法在定量与定性评估中均优于现有先进方法。代码发布于 \url{https://github.com/shendb2022/HTD-Mamba}。