AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2507.16172v1
• Date: Tue, 22 Jul 2025 02:36:16 GMT
• Title: AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection
• Authors: Tao Wang, Tiecheng Bai, Chao Xu, Bin Liu, Erlei Zhang, Jiyun Huang, Hongming Zhang,
• Abstract summary: We propose a novel model, AtrousMamba, which balances the extraction of fine-grained local details with the integration of global contextual information.<n>By leveraging the atrous window scan visual state space (AWVSS) module, we design dedicated end-to-end Mamba-based frameworks for binary change detection (BCD) and semantic change detection (SCD)<n> Experimental results on six benchmark datasets show that the proposed framework outperforms existing CNN-based, Transformer-based, and Mamba-based methods.
• Score: 29.004019252136565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, a novel visual state space (VSS) model, referred to as Mamba, has demonstrated significant progress in modeling long sequences with linear complexity, comparable to Transformer models, thereby enhancing its adaptability for processing visual data. Although most methods aim to enhance the global receptive field by directly modifying Mamba's scanning mechanism, they tend to overlook the critical importance of local information in dense prediction tasks. Additionally, whether Mamba can effectively extract local features as convolutional neural networks (CNNs) do remains an open question that merits further investigation. In this paper, We propose a novel model, AtrousMamba, which effectively balances the extraction of fine-grained local details with the integration of global contextual information. Specifically, our method incorporates an atrous-window selective scan mechanism, enabling a gradual expansion of the scanning range with adjustable rates. This design shortens the distance between adjacent tokens, enabling the model to effectively capture fine-grained local features and global context. By leveraging the atrous window scan visual state space (AWVSS) module, we design dedicated end-to-end Mamba-based frameworks for binary change detection (BCD) and semantic change detection (SCD), referred to as AWMambaBCD and AWMambaSCD, respectively. Experimental results on six benchmark datasets show that the proposed framework outperforms existing CNN-based, Transformer-based, and Mamba-based methods. These findings clearly demonstrate that Mamba not only captures long-range dependencies in visual data but also effectively preserves fine-grained local details.

Related papers

• MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification [4.14360329494344]
  We introduce MambaOutRS, a novel hybrid convolutional architecture for remote sensing image classification.<n>MambaOutRS builds upon stacked Gated CNN blocks for local feature extraction and introduces a novel Fourier Filter Gate (FFG) module.
  arXiv  Detail & Related papers  (2025-06-24T12:20:11Z)
• RD-UIE: Relation-Driven State Space Modeling for Underwater Image Enhancement [59.364418120895]
  Underwater image enhancement (UIE) is a critical preprocessing step for marine vision applications.<n>We develop a novel relation-driven Mamba framework for effective UIE (RD-UIE)<n>Experiments on underwater enhancement benchmarks demonstrate RD-UIE outperforms the state-of-the-art approach WMamba.
  arXiv  Detail & Related papers  (2025-05-02T12:21:44Z)
• 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)
• VADMamba: Exploring State Space Models for Fast Video Anomaly Detection [4.874215132369157]
  VQ-Mamba Unet (VQ-MaU) framework incorporates a Vector Quantization (VQ) layer and Mamba-based Non-negative Visual State Space (NVSS) block.<n>Results validate the efficacy of the proposed VADMamba across three benchmark datasets.
  arXiv  Detail & Related papers  (2025-03-27T05:38:12Z)
• GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model [4.4735289317146405]
  GLADMamba is a novel framework that adapts the selective state space model into UGLAD field.<n>To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD.
  arXiv  Detail & Related papers  (2025-03-23T02:40:17Z)
• 2DMCG:2DMambawith Change Flow Guidance for Change Detection in Remote Sensing [4.18306618346671]
  This paper proposes an efficient framework based on a Vision Mamba variant that enhances its ability to capture 2D spatial information.<n>The framework employs a 2DMamba encoder to effectively learn global contextual spatial information from multi-temporal images.<n>Experiments on benchmark datasets demonstrate the superior performance of our framework compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-01T14:55:13Z)
• DAMamba: Vision State Space Model with Dynamic Adaptive Scan [51.81060691414399]
  State space models (SSMs) have recently garnered significant attention in computer vision.<n>We propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions.<n>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)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction.<n>We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation.<n>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)
• CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection [30.387208446303944]
  We propose a model called CDMamba, which effectively combines global and local features for handling binary CD tasks.<n>Specifically, the Scaled ResidualMamba block is proposed to utilize the ability of Mamba to extract global features and convolution to enhance the local details.
  arXiv  Detail & Related papers  (2024-06-06T16:04:30Z)
• MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection [72.46396769642787]
  We develop a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient infrared small target detection. MiM-ISTD is $8 times$ faster than the SOTA method and reduces GPU memory usage by 62.2$%$ when testing on $2048 times 2048$ images.
  arXiv  Detail & Related papers  (2024-03-04T15:57:29Z)

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.