Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2408.01372v3
• Date: Sat, 30 Nov 2024 13:24:19 GMT
• Title: Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Muhammad Usama, Swalpa Kumar Roy, Jocelyn Chanussot, Danfeng Hong,
• Abstract summary: We propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba) MorpMamba combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models.
• Score: 27.943537708598306
• License:
• Abstract: Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffer from inefficiencies, as their computational complexity scales quadratically with sequence length. To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. In MorpMamba, a novel token generation module first converts HSI patches into spatial-spectral tokens. These tokens are then processed through morphological operations such as erosion and dilation, utilizing depthwise separable convolutions to capture structural and shape information. A token enhancement module refines these features by dynamically adjusting the spatial and spectral tokens based on central HSI regions, ensuring effective feature fusion within each block. Subsequently, multi-head self-attention is applied to further enrich the feature representations, allowing the model to capture complex relationships and dependencies within the data. Finally, the enhanced tokens are fed into a state space module, which efficiently models the temporal evolution of the features for classification. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models while maintaining high accuracy. The code will be made publicly available at \url{https://github.com/mahmad000/MorpMamba}.

Related papers

• DAMamba: Vision State Space Model with Dynamic Adaptive Scan [51.81060691414399]
  State space models (SSMs) have recently garnered significant attention in computer vision. We propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions. Based on DAS, we propose the vision backbone DAMamba, which significantly outperforms current state-of-the-art vision Mamba models in vision tasks.
  arXiv  Detail & Related papers  (2025-02-18T08:12:47Z)
• 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)
• STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection [48.997518615379995]
  Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. Most existing methods based on CNNs and transformers still suffer from substantial computational burdens. We propose a lightweight and effective Mamba-based network named STNMamba to enhance the learning of spatial-temporal normality.
  arXiv  Detail & Related papers  (2024-12-28T08:49:23Z)
• MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation [6.673169053236727]
  We propose MambaClinix, a novel U-shaped architecture for medical image segmentation. MambaClinix integrates a hierarchical gated convolutional network with Mamba in an adaptive stage-wise framework. Our results show that MambaClinix achieves high segmentation accuracy while maintaining low model complexity.
  arXiv  Detail & Related papers  (2024-09-19T07:51:14Z)
• Microscopic-Mamba: Revealing the Secrets of Microscopic Images with Just 4M Parameters [12.182070604073585]
  CNNs struggle with modeling long-range dependencies, limiting their ability to fully utilize semantic information in images. Transformers are hampered by the complexity of quadratic computations. We propose a model based on the Mamba architecture: Microscopic-Mamba.
  arXiv  Detail & Related papers  (2024-09-12T10:01:33Z)
• Cross-Scan Mamba with Masked Training for Robust Spectral Imaging [51.557804095896174]
  We propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding. Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification [4.389334324926174]
  This study introduces the innovative Mamba-in-Mamba (MiM) architecture for HSI classification, the first attempt of deploying State Space Model (SSM) in this task. MiM model includes 1) A novel centralized Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2) A Tokenized Mamba (T-Mamba) encoder, and 3) A Weighted MCS Fusion (WMF) module. Experimental results from three public HSI datasets demonstrate that our method outperforms existing baselines and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-05-20T13:19:02Z)
• 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)
• Learning Modulated Transformation in GANs [69.95217723100413]
  We equip the generator in generative adversarial networks (GANs) with a plug-and-play module, termed as modulated transformation module (MTM) MTM predicts spatial offsets under the control of latent codes, based on which the convolution operation can be applied at variable locations. It is noteworthy that towards human generation on the challenging TaiChi dataset, we improve the FID of StyleGAN3 from 21.36 to 13.60, demonstrating the efficacy of learning modulated geometry transformation.
  arXiv  Detail & Related papers  (2023-08-29T17:51:22Z)
• Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression [63.56922682378755]
  We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform. Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
  arXiv  Detail & Related papers  (2023-08-17T01:34:51Z)
• STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition [50.064502884594376]
  We study the problem of human action recognition using motion capture (MoCap) sequences. We propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models.
  arXiv  Detail & Related papers  (2023-03-31T16:19:27Z)

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.