Physics-Aligned Spectral Mamba: Decoupling Semantics and Dynamics for Few-Shot Hyperspectral Target Detection
Luqi Gong, Qixin Xie, Yue Chen, Ziqiang Chen, Fanda Fan, Shuai Zhao, Chao Li

TL;DR
This paper introduces SpecMamba, a frequency-aware, parameter-efficient framework for few-shot hyperspectral target detection that decouples semantic and spectral features to improve accuracy and generalization.
Contribution
SpecMamba employs a DCT-based spectral adapter, a prior-guided encoder, and self-supervised test-time adaptation to enhance hyperspectral detection performance.
Findings
Outperforms state-of-the-art methods in detection accuracy.
Improves cross-domain generalization in hyperspectral target detection.
Efficiently captures global spectral dependencies without full fine-tuning.
Abstract
Meta-learning facilitates few-shot hyperspectral target detection (HTD), but adapting deep backbones remains challenging. Full-parameter fine-tuning is inefficient and prone to overfitting, and existing methods largely ignore the frequency-domain structure and spectral band continuity of hyperspectral data, limiting spectral adaptation and cross-domain generalization.To address these challenges, we propose SpecMamba, a parameter-efficient and frequency-aware framework that decouples stable semantic representation from agile spectral adaptation. Specifically, we introduce a Discrete Cosine Transform Mamba Adapter (DCTMA) on top of frozen Transformer representations. By projecting spectral features into the frequency domain via DCT and leveraging Mamba's linear-complexity state-space recursion, DCTMA explicitly captures global spectral dependencies and band continuity while avoiding the…
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