HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2504.15612v1
• Date: Tue, 22 Apr 2025 06:13:02 GMT
• Title: HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification
• Authors: Hongxing Peng, Kang Lin, Huanai Liu,
• Abstract summary: We propose a full-field interaction multi-groups Mamba framework (HS-Mamba) for classification of hyperspectral images.<n>HS-Mamba consists of a dual-channel spatial-spectral encoder (DCSS-encoder) module and a lightweight global inline attention (LGI-Att) branch.<n>Extensive experiments demonstrate the superiority of the proposed HS-Mamba, outperforming state-of-the-art methods on four benchmark HSI datasets.
• Score: 1.9526430269580959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral image (HSI) classification has been one of the hot topics in remote sensing fields. Recently, the Mamba architecture based on selective state-space models (S6) has demonstrated great advantages in long sequence modeling. However, the unique properties of hyperspectral data, such as high dimensionality and feature inlining, pose challenges to the application of Mamba to HSI classification. To compensate for these shortcomings, we propose an full-field interaction multi-groups Mamba framework (HS-Mamba), which adopts a strategy different from pixel-patch based or whole-image based, but combines the advantages of both. The patches cut from the whole image are sent to multi-groups Mamba, combined with positional information to perceive local inline features in the spatial and spectral domains, and the whole image is sent to a lightweight attention module to enhance the global feature representation ability. Specifically, HS-Mamba consists of a dual-channel spatial-spectral encoder (DCSS-encoder) module and a lightweight global inline attention (LGI-Att) branch. The DCSS-encoder module uses multiple groups of Mamba to decouple and model the local features of dual-channel sequences with non-overlapping patches. The LGI-Att branch uses a lightweight compressed and extended attention module to perceive the global features of the spatial and spectral domains of the unsegmented whole image. By fusing local and global features, high-precision classification of hyperspectral images is achieved. Extensive experiments demonstrate the superiority of the proposed HS-Mamba, outperforming state-of-the-art methods on four benchmark HSI datasets.

Related papers

• DefMamba: Deformable Visual State Space Model [65.50381013020248]
  We propose a novel visual foundation model called DefMamba.<n>By combining a deformable scanning(DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details.<n>Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks.
  arXiv  Detail & Related papers  (2025-04-08T08:22:54Z)
• MatIR: A Hybrid Mamba-Transformer Image Restoration Model [95.17418386046054]
  We propose a Mamba-Transformer hybrid image restoration model called MatIR. MatIR cross-cycles the blocks of the Transformer layer and the Mamba layer to extract features. In the Mamba module, we introduce the Image Inpainting State Space (IRSS) module, which traverses along four scan paths.
  arXiv  Detail & Related papers  (2025-01-30T14:55:40Z)
• 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)
• Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging [40.80197280147993]
  We propose a Mamba-inspired Joint Unfolding Network (MiJUN) to overcome the inherent nonlinear and ill-posed characteristics of HSI reconstruction. We introduce an accelerated unfolding network scheme, which reduces the reliance on initial optimization stages. We refine the scanning strategy with Mamba by integrating the tensor mode-$k$ unfolding into the Mamba network.
  arXiv  Detail & Related papers  (2025-01-02T13:56:23Z)
• Revisiting the Integration of Convolution and Attention for Vision Backbone [59.50256661158862]
  Convolutions and multi-head self-attentions (MHSAs) are typically considered alternatives to each other for building vision backbones. We propose in this work to use MSHAs and Convs in parallel textbfat different granularity levels instead. We empirically verify the potential of the proposed integration scheme, named textitGLMix: by offloading the burden of fine-grained features to light-weight Convs, it is sufficient to use MHSAs in a few semantic slots.
  arXiv  Detail & Related papers  (2024-11-21T18:59:08Z)
• 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)
• 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)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• RSMamba: Remote Sensing Image Classification with State Space Model [25.32283897448209]
  We introduce RSMamba, a novel architecture for remote sensing image classification. RSMamba is based on the State Space Model (SSM) and incorporates an efficient, hardware-aware design known as the Mamba. We propose a dynamic multi-path activation mechanism to augment Mamba's capacity to model non-temporal image data.
  arXiv  Detail & Related papers  (2024-03-28T17:59:49Z)
• Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images [108.79667788962425]
  salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. We propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features.
  arXiv  Detail & Related papers  (2021-12-02T04:46:40Z)

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.