MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2504.20509v1
• Date: Tue, 29 Apr 2025 07:50:36 GMT
• Title: MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification
• Authors: Yichu Xu, Di Wang, Hongzan Jiao, Lefei Zhang, Liangpei Zhang,
• Abstract summary: Mamba model has strong potential in hyperspectral image (HSI) classification.<n>We propose MambaMoE, a novel spectral-spatial mixture-of-experts framework.<n>We show that MambaMoE achieves state-of-the-art performance in both accuracy and efficiency.
• Score: 32.34863609876265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mamba model has recently demonstrated strong potential in hyperspectral image (HSI) classification, owing to its ability to perform context modeling with linear computational complexity. However, existing Mamba-based methods usually neglect the spectral and spatial directional characteristics related to heterogeneous objects in hyperspectral scenes, leading to limited classification performance. To address these issues, we propose MambaMoE, a novel spectral-spatial mixture-of-experts framework, representing the first MoE-based approach in the HSI classification community. Specifically, we design a Mixture of Mamba Expert Block (MoMEB) that leverages sparse expert activation to enable adaptive spectral-spatial modeling. Furthermore, we introduce an uncertainty-guided corrective learning (UGCL) strategy to encourage the model's attention toward complex regions prone to prediction ambiguity. Extensive experiments on multiple public HSI benchmarks demonstrate that MambaMoE achieves state-of-the-art performance in both accuracy and efficiency compared to existing advanced approaches, especially for Mamba-based methods. Code will be released.

Related papers

• Sparse Deformable Mamba for Hyperspectral Image Classification [1.3471768511567523]
  Mamba models significantly improve hyperspectral image (HSI) classification. One critical challenge is the difficulty in building the sequence of Mamba tokens efficiently. This paper presents a Sparse Deformable Mamba (SDMamba) approach for enhanced HSI classification.
  arXiv  Detail & Related papers  (2025-04-13T06:08:19Z)
• MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification [46.111607032455225]
  We propose a novel HSI classification model based on a Mamba model, named MambaHSI.<n> Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level.<n>We propose a spectral Mamba block (SpeMB) to split the spectral vector into multiple groups, mine the relations across different spectral groups, and extract spectral features.
  arXiv  Detail & Related papers  (2025-01-09T03:27:47Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision.<n>In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. State Space Models (SSMs) have achieved notable success in computer vision. We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking [51.28485682954006]
  We propose a pure Mamba-based framework (MambaVT) to fully exploit intrinsic-temporal contextual modeling for robust visible-thermal tracking. Specifically, we devise the long-range cross-frame integration component to globally adapt to target appearance variations. Experiments show the significant potential of vision Mamba for RGB-T tracking, with MambaVT achieving state-of-the-art performance on four mainstream benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T02:29:00Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications.<n>We show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies.<n>For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• MambaLRP: Explaining Selective State Space Sequence Models [18.133138020777295]
  Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest.<n>These models allow efficient processing of long sequences in linear time and are rapidly being adopted in a wide range of applications such as language modeling.<n>To foster their reliable use in real-world scenarios, it is crucial to augment their transparency.
  arXiv  Detail & Related papers  (2024-06-11T12:15:47Z)
• DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification [10.329381824237434]
  We propose a novel lightweight parallel design called lightweight dual-stream Mamba-convolution network (DualMamba) for HSI classification. Specifically, a parallel lightweight Mamba and CNN block are first developed to extract global and local spectral-spatial features. Compared with state-of-the-art HSI classification methods, experimental results demonstrate that DualMamba achieves significant classification accuracy.
  arXiv  Detail & Related papers  (2024-06-11T08:26:42Z)
• Spectral-Spatial Mamba for Hyperspectral Image Classification [23.215920983979426]
  spectral-spatial Mamba (SS-Mamba) is applied to hyperspectral image (HSI) classification. The proposed SS-Mamba mainly consists of spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. The experimental results conducted on widely used HSI datasets reveal that the proposed model achieves competitive results.
  arXiv  Detail & Related papers  (2024-04-29T03:36:05Z)

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.