Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2408.01224v3
• Date: Mon, 26 Aug 2024 09:59:55 GMT
• Title: Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama, Hamad Ahmed Altuwaijri, Manuel Mazzara, Salvatore Distefano,
• Abstract summary: Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies. We propose the SSM with multi-head self-attention and token enhancement (MHSSMamba) MHSSMamba achieved remarkable classification accuracies of 97.62% on Pavia University, 96.92% on the University of Houston, 96.85% on Salinas, and 99.49% on Wuhan-longKou datasets.
• Score: 3.105394345970172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention and token enhancement (MHSSMamba). This model integrates spectral and spatial information by enhancing spectral tokens and using multi-head attention to capture complex relationships between spectral bands and spatial locations. It also manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved remarkable classification accuracies of 97.62\% on Pavia University, 96.92\% on the University of Houston, 96.85\% on Salinas, and 99.49\% on Wuhan-longKou datasets. The source code is available at \href{https://github.com/MHassaanButt/MHA\_SS\_Mamba}{GitHub}.

Related papers

• MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification [46.111607032455225]
  We propose a novel HSI classification model based on a Mamba model, named MambaHSI. Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level. 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)
• WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification [3.5302264121619094]
  This paper introduces WaveMamba, a novel approach that integrates wavelet transformation with the spatial-spectral Mamba architecture to enhance HSI classification. WaveMamba surpasses existing models, achieving an accuracy improvement of 4.5% on the University of Houston dataset and a 2.0% increase on the Pavia University dataset.
  arXiv  Detail & Related papers  (2024-08-02T12:44:07Z)
• GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification [19.740333867168108]
  GraphMamba is an efficient graph structure learning vision Mamba classification framework to achieve deep spatial-spectral information mining. The core components of GraphMamba include the HyperMamba module for improving computational efficiency and the SpectralGCN module for adaptive spatial context awareness.
  arXiv  Detail & Related papers  (2024-07-11T07:56:08Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation. It integrates spatial and spectral features using a specially designed spectral enhancement module. It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging [102.35787741640749]
  We propose a novel Dual Hyperspectral Mamba (DHM) to explore both global long-range dependencies and local contexts for efficient HSI reconstruction. Specifically, our DHM consists of multiple dual hyperspectral S4 blocks (DHSBs) to restore original HSIs.
  arXiv  Detail & Related papers  (2024-06-01T14:14:40Z)
• Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution [49.902047563260496]
  We develop the first attempt to integrate the Vision State Space Model (Mamba) for remote sensing image (RSI) super-resolution. To achieve better SR reconstruction, building upon Mamba, we devise a Frequency-assisted Mamba framework, dubbed FMSR. Our FMSR features a multi-level fusion architecture equipped with the Frequency Selection Module (FSM), Vision State Space Module (VSSM), and Hybrid Gate Module (HGM)
  arXiv  Detail & Related papers  (2024-05-08T11:09:24Z)
• 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)
• SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification [35.52272615695294]
  We propose a spatial-spectral masked auto-encoder (SS-MAE) for HSI and LiDAR/SAR data joint classification. Our SS-MAE fully exploits the spatial and spectral representations of the input data. To complement local features in the training stage, we add two lightweight CNNs for feature extraction.
  arXiv  Detail & Related papers  (2023-11-08T03:54:44Z)
• Hyperspectral Image Segmentation based on Graph Processing over Multilayer Networks [51.15952040322895]
  One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features. We propose several approaches to HSI segmentation based on M-GSP feature extraction. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
  arXiv  Detail & Related papers  (2021-11-29T23:28:18Z)
• Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction [127.20208645280438]
  Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
  arXiv  Detail & Related papers  (2021-11-15T16:59:48Z)

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.