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

• URL: http://arxiv.org/abs/2604.05562v1
• Date: Tue, 07 Apr 2026 08:04:18 GMT
• Title: Physics-Aligned Spectral Mamba: Decoupling Semantics and Dynamics for Few-Shot Hyperspectral Target Detection
• Abstract summary: SpecMamba is a framework that decouples stable semantic representation from agile spectral adaptation.<n>It consistently outperforms state-of-the-art methods in detection accuracy and cross-domain generalization.
• Score: 14.607792636586067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SuppreSensing: Expert-Guided Feature Recalibration and Discrepancy Augmentation for Multimodal Object Detection [19.446402308279676]
  We propose SuppreSensing, which reformulates multimodal fusion as a selective collaboration process.<n>SupreSensing first designs an Expert-driven Multimodal Feature Recalibration (EMFR) module.<n>A modality-specific augmentation strategy is employed to attribute specific modality features.<n>Experiments on the DroneVehicle and VEDAI datasets demonstrate that SuppreSensing achieves state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2026-08-21T10:06:00Z)
• MSP-Net: Manifold-Guided Spectral Prompt Network for Hyperspectral Object Tracking [11.03462046191901]
  Hyperspectral object tracking offers unique advantages for target discrimination in complex scenes.<n>Existing methods treat hyperspectral images as multi-channel extensions of RGB images, performing feature fusion in fixed band order.<n>We propose the Manifold-Guided Spectral Prompt Network (MSP-Net)<n> Experiments on HOT2020 and HOT2023 demonstrate MSP-Net achieves AUC and Precision exceeding 0.80 and 0.96, respectively.
  arXiv  Detail & Related papers  (2026-08-10T13:09:25Z)
• SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity [57.84199526590779]
  Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that hampers global convergence.<n>In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift.<n>Motivated by this, we propose SpecGradFilter, a unified Spectral Filtering Gradient Framework that tames heterogeneity by suppressing discordant low-frequency signals.
  arXiv  Detail & Related papers  (2026-07-05T09:23:35Z)
• Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation [58.33732779555114]
  We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities.<n>We propose Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation (SpectraMB)<n>SpectraMB achieves the best results in most evaluation settings and exhibits improved robustness under noisy interactions.
  arXiv  Detail & Related papers  (2026-06-01T15:58:13Z)
• Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs [59.973833105841685]
  A key challenge for improving adversarial transferability is to effectively capture the intrinsic visual focus shared across different models.<n>We propose FRA-Attack, which addresses both challenges from a unified frequency-domain regularization perspective.<n>For feature alignment, a high-pass DCT objective on patch features suppresses redundant global structures and concentrates the loss on the high-frequency band that carries the MLLMs' intrinsic visual focus.<n>For gradient optimization, we introduce Frequency-domain Gradient Regularization (FGR), a textitmodel-agnostic low-pass regularizer that modulates the surrogate gradient using
  arXiv  Detail & Related papers  (2026-05-20T08:15:56Z)
• WILD-SAM: Phase-Aware Expert Adaptation of SAM for Landslide Detection in Wrapped InSAR Interferograms [5.326496857825592]
  We propose WILD-SAM, a novel parameter-efficient fine-tuning framework for landslide detection on wrapped interferograms.<n>The architecture integrates a Phase-Aware Mixture-of-Experts (PA-MoE) Adapter into the frozen encoder to align spectral distributions.<n>Experiments on the ISSLIDE and ISSLIDE+ benchmarks demonstrate that WILD-SAM achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-16T02:16:29Z)
• Spectral Property-Driven Data Augmentation for Hyperspectral Single-Source Domain Generalization [13.335941336922446]
  Hyperspectral images benefit from numerous spectral channels that provide rich information for classification.<n>The increased dimensionality and sensor variability make them more sensitive to distributional discrepancies across domains.<n>We propose a spectral property-driven data augmentation (SPDDA) that explicitly accounts for the inherent properties of HSI.
  arXiv  Detail & Related papers  (2026-03-17T15:30:28Z)
• SONIC: Spectral Oriented Neural Invariant Convolutions [0.0]
  Convolutional Neural Networks (CNNs) rely on fixed-size kernels scanning local patches.<n>ViTs provide global connectivity but lack spatial inductive bias, depend on explicit positional encodings, and remain tied to the initial patch size.<n>We introduce SONIC, a continuous spectral parameterisation that models convolutional operators using a small set of shared, orientation-selective components.
  arXiv  Detail & Related papers  (2026-01-27T18:51:11Z)
• Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification [5.784164305429653]
  This paper proposes a self-supervised cross-domain transfer framework.<n>It learns transferable spectral-spatial joint representations without source labels.<n> Experimental results demonstrate stable classification performance and strong cross-domain adaptability.
  arXiv  Detail & Related papers  (2026-01-26T02:52:35Z)
• WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition [61.3530659856013]
  We propose a novel decoder architecture, WaveSeg, which jointly optimize feature refinement in spatial and wavelet domains.<n>High-frequency components are first learned from input images as explicit priors to reinforce boundary details.<n>Experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-10-24T01:41:31Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios [54.58186816693791]
  environments constantly change over time and space, posing significant challenges for object detectors trained based on a closed-set assumption.<n>We propose a new mechanism, converting the fine-tuning process to a specific- parameter generation.<n>In particular, we first design a dual-path LoRA-based domain-aware adapter that disentangles features into domain-invariant and domain-specific components.
  arXiv  Detail & Related papers  (2025-06-30T17:14:12Z)
• Evidential Deep Learning with Spectral-Spatial Uncertainty Disentanglement for Open-Set Hyperspectral Domain Generalization [5.770351255180493]
  We propose a novel open-set domain generalization framework for hyperspectral image classification.<n>The framework combines four key components: Spectrum-Invariant Frequency Disentanglement (SIFD), Dual-Channel Residual Network (DCRN), Evidential Deep Learning (EDL) and Spectral-Spatial Uncertainty Disentanglement (SSUD)<n> Experimental results show that our approach achieves performance comparable to state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2025-06-11T07:07:14Z)
• AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection [49.81255045696323]
  We present the Auxiliary Metadata Driven Infrared Small Target Detector (AuxDet)<n>AuxDet integrates metadata semantics with visual features, guiding adaptive representation learning for each sample.<n>Experiments on the challenging WideIRSTD-Full benchmark demonstrate that AuxDet consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-21T07:02:05Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.