HTD-Mamba: Efficient Hyperspectral Target Detection with Pyramid State Space Model

TL;DR

HTD-Mamba introduces a self-supervised hyperspectral target detection method using a pyramid state space model that effectively captures spectral and spatial features, outperforming existing approaches.

Contribution

The paper presents a novel pyramid state space model with spectral contrastive learning and spatial-encoded spectral augmentation for improved hyperspectral target detection.

Findings

Outperforms state-of-the-art methods on four datasets.

Effectively models long-range spectral dependencies.

Enhances robustness to spectral variation.

Abstract

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}.