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

cs.CVApr 7, 2026

Physics-Aligned Spectral Mamba: Decoupling Semantics and Dynamics for Few-Shot Hyperspectral Target Detection

Authors: Luqi Gong, Qixin Xie, Yue Chen, Ziqiang Chen, Fanda Fan, Shuai Zhao, Chao Li

Organizations: Research Center for Space Computing System, Zhejiang Lab, Hangzhou 311121, China · State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

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 redundancy of full fine-tuning. Furthermore, to address prototype drift caused by limited sample sizes, we design a Prior-Guided Tri-Encoder (PGTE) that allows laboratory spectral priors to guide the optimization of the learnable adapter without disrupting the stable semantic feature space. Finally, a Self-Supervised Pseudo-Label Mapping (SSPLM) strategy is developed for test-time adaptation, enabling efficient decision boundary refinement through uncertainty-aware sampling and dual-path consistency constraints. Extensive experiments on multiple public datasets demonstrate that SpecMamba consistently outperforms state-of-the-art methods in detection accuracy and cross-domain generalization.

Figures & tables

Fig. 1: Motivation of the proposed SpecMamba. Conventional direct spectral modeling often leads to boundary ambiguity, while the proposed frequency-conditioned modeling reorganizes HSI representations in the frequency domain to effectively separate targets from background.

Fig. 2: Overview of the proposed SpecMamba framework. It consists of three stages: data processing, dual-stream spectral-spatial meta-learning with DCTMA and PGTE, and target-domain inference with SSPLM.

Fig. 3: Detailed architecture of the selective SSM in the DCTMA module. It combines DCT-based frequency decomposition with Mamba-based state-space modeling to capture long-range spectral dependencies and suppress background clutter.

Fig. 4: Pseudo-color image and Ground truth of the Source data (Chikusei).

Fig. 5: Dataset image scenes and ground truth. (a) San Diego I. (b) San Diego I. (c) Cir. (d) Urban.

HSI Data Set	Metric	CEM [ 26 ]	ACE [ 23 ]	MLSN [ 61 ]	BLTSC [ 62 ]	HTD-IRN [ 63 ]	UEML [ 53 ]	SelfMTL [ 64 ]	HTD-Mamba [ 65 ]	Ours
San Diego I	AUC $(P_{F},P_{D})\,\uparrow$	0.99751	0.99455	0.98844	0.99325	0.99613	0.99597	0.97869	0.99861	0.99927
	AUC $(\tau,P_{D})\,\uparrow$	0.88664	0.98047	0.89927	0.83205	0.65967	0.84379	0.17207	0.95748	0.98220
	AUC $(\tau,P_{F})\,\downarrow$	0.10539	0.07026	0.53271	0.23976	0.01859	0.10251	0.00455	0.14622	0.16227
	AUC ${}_{\text{OA}}\,\uparrow$	1.77877	1.27244	1.35500	1.58554	1.63721	1.73725	1.14622	1.80986	1.81919
	AUC ${}_{\text{SNPR}}\,\uparrow$	8.41339	11.39553	1.68811	3.47033	35.48498	8.23123	37.86353	6.54818	6.05276
San Diego II	AUC $(P_{F},P_{D})\,\uparrow$	0.91535	0.94235	0.89938	0.97314	0.94725	0.86952	0.90251	0.97585	0.98915

TABLE I: Quantitative comparison of different methods on four HSI datasets

Fig. 6: Experimental datasets and visualized detection maps of competing methods.

Fig. 7: ROC curves for each detection method on the four datasets, from left to right: 3-D ROC curves, 2-D ROC curves $(P_{F},P_{D})$ , 2-D ROC curves $(\tau,P_{F})$ , and 2-D ROC curves $(\tau,P_{D})$ .

Fig. 8: Target–background separation diagrams of competing methods on four datasets.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Fig. 9: Fig. S1. Sensitivity analysis of SpecMamba with respect to four key parameters on the four benchmark datasets.

$\rho_{L}$	$\rho_{M}$	AV-I	AV-II	Cri	Urban-1
0.20	0.50	0.9894	0.9821	0.9968	0.9912
0.25	0.60	0.9936	0.9891	0.9997	0.9994
0.30	0.65	0.9921	0.9862	0.9989	0.9982
0.35	0.70	0.9897	0.9815	0.9961	0.9954

Appendix

TABLE II: Table S1. Sensitivity analysis of frequency partition ratios (

\rho_{L},\rho_{M}

) in the DCTMA module (AUC).

Fig. 10: Fig. S2. Sensitivity analysis of pseudo-label confidence thresholds and joint loss weights in SpecMamba.

Variant	DCTMA	PGTE	SSPLM	AV-I	AV-II	Cri	Urban-1
Case 1	✓			0.9886	0.9673	0.9876	0.9876
Case 2		✓		0.9936	0.9718	0.9877	0.9682
Case 3	✓	✓		0.9864	0.9731	0.9886	0.9832
Ours	✓	✓	✓	0.9992	0.9891	0.9999	0.9994

Appendix

TABLE III: Table S2. Ablation analysis of the core components (AUC) across four benchmark datasets.

Module	Sub-Strategy	AV-II	Urban-1
DCTMA	w/o Freq. Masking	0.9745	0.9812
DCTMA	Multi-Freq Partition	0.9891	0.9994
SSPLM	Only Spatial Aug.	0.9812	0.9904
	Only Spectral Aug.	0.9834	0.9921
	Hybrid Aug.	0.9891	0.9994

Appendix

TABLE IV: Table S3. Analysis of module design details (AUC).

Explore similar work

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models
Jul 14, 2026Mingzhen Xu, Haonan Guo, Di Wang +9Hyperspectral Image Classification Parameter-Efficient Fine-Tuning
School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China · China Petroleum Pipeline Engineering Corporation, Langfang, China +6
HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone
May 17, 2026Guanyiman Fu, Jingtao Li, Zihang Cheng +8Vision Foundation Model Adaptation Hyperspectral Imaging
Griffith University, Australia · Wuhan University, China · Nanjing University of Science and Technology, China +2
VP-Hype: A Hybrid Mamba-Transformer Framework with Visual-Textual Prompting for Hyperspectral Image Classification
Mar 1, 2026Abdellah Zakaria Sellam, Fadi Abdeladhim Zidi, Salah Eddine Bekhouche +4Hyperspectral Image Classification Hyperspectral Imaging
Institute of Applied Sciences and Intelligent Systems (ISASI), CNR, 73100 Lecce, Italy · Dept. of Engineering for Innovation, University of Salento, 73100 Lecce, Italy · VSC Laboratory, Department of Electronics and Automation, University of Biskra, Biskra, Algeria +4

Jul 14, 2026J/K move · Enter open · S save

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

Mingzhen Xu, Haonan Guo, Di Wang, Yinghua Qu+8

School of Computer Science, Wuhan University, Wuhan, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China · China Petroleum Pipeline Engineering Corporation, Langfang, China+6

Hyperspectral Image Classification Parameter-Efficient Fine-Tuning Modular Neural Networks

Open paper AskPDF arXiv

Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream hyperspectral image (HSI) classification tasks with limited labeled samples. However, spectral band configurations vary substantially across sensors, which makes direct model transfer difficult. Existing adaptation strategies often compress, select, or reshape the original spectra to match model-specific input requirements. These operations may discard useful spectral information and weaken local spectral continuity. To address this problem, we propose MBTI, a Multi-Branch efficient fine-tuning framework for Hyperspectral Image classification. MBTI adapts hyperspectral foundation models to downstream classification tasks while preserving full-band spectral information. First, we introduce a spectral-continuity-preserving multi-branch preprocessing strategy. The original HSI is divided into multiple continuous spectral subsets, and a band reuse mechanism is used when the remaining bands cannot form a complete branch. This avoids invalid padding and unnecessary spectral loss. Second, independent Low-Rank Adaptation (LoRA) modules are inserted into each branch. They enable different spectral intervals to learn task-specific discriminative features while keeping most pre-trained parameters frozen. Finally, a multi-branch channel attention fusion module adaptively recalibrates and integrates features from all spectral branches. Experiments on three public hyperspectral datasets show that MBTI achieves competitive and superior performance compared with representative classification methods. Under the final rank-8 configuration, only about 2.33%--2.36% of the parameters are trainable. The code will be available at https://github.com/Azhenmiddleblock/MBTI/tree/main.

Abstract

Explore similar work

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone

VP-Hype: A Hybrid Mamba-Transformer Framework with Visual-Textual Prompting for Hyperspectral Image Classification