  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 variation, 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 center 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}.
