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RS-Mamba for Large Remote Sensing Image Dense Prediction

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arxiv 2404.02668 v2 pith:UXTQB3U5 submitted 2024-04-03 cs.CV

Sijie Zhao , Hao Chen , Xueliang Zhang , Pengfeng Xiao , Lei Bai , Wanli Ouyang This is my paper · ORCID
classification cs.CV
keywords remotesensingimageslargedensepredictiontaskscontext
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Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess global modeling capabilities, they encounter computational challenges when applied to large VHR images due to their quadratic complexity. The conventional practice of cropping large images into smaller patches results in a notable loss of contextual information. To address these issues, we propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images. Considering that the land covers in remote sensing images are distributed in arbitrary spatial directions due to characteristics of remote sensing over-head imaging, the RSM incorporates an omnidirectional selective scan module to globally model the context of images in multiple directions, capturing large spatial features from various directions. Extensive experiments on semantic segmentation and change detection tasks across various land covers demonstrate the effectiveness of the proposed RSM. We designed simple yet effective models based on RSM, achieving state-of-the-art performance on dense prediction tasks in VHR remote sensing images without fancy training strategies. Leveraging the linear complexity and global modeling capabilities, RSM achieves better efficiency and accuracy than transformer-based models on large remote sensing images. Interestingly, we also demonstrated that our model generally performs better with a larger image size on dense prediction tasks. Our code is available at https://github.com/walking-shadow/Official_Remote_Sensing_Mamba.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A bidirectional, multi-scale Mamba scan for cross-task interaction improves multi-task dense prediction accuracy on NYUD-V2 and PASCAL-Context over prior state-of-the-art methods.

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

    cs.CV 2025-07 conditional novelty 6.0 of 10

    An atrous-window scanning strategy improves Mamba-based change detection on six remote sensing benchmarks, showing visual state space models can capture fine local details alongside global context.

  3. CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model

    cs.CV 2025-01 conditional novelty 6.0 of 10

    CD-Lamba introduces adaptive top-k window selection and pixel-wise cross-temporal scanning for Mamba-based remote sensing change detection, achieving state-of-the-art F1 on WHU-CD, SYSU-CD, DSIFN-CD, and CLCD.

  4. SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    SAMamba, combining a frozen SAM2/Hiera encoder with Vision Mamba blocks and three lightweight modules, achieves state-of-the-art infrared small target detection on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST.

  5. A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation

    eess.IV 2025-01 conditional novelty 5.0 of 10

    SCSM, a scene coupling and semantic mask attention decoder, reports higher accuracy than prior methods on four remote sensing segmentation benchmarks with lower computational cost.

  6. A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement

    eess.IV 2025-04 conditional novelty 4.0 of 10

    HREFNet combines a high-resolution backbone, dynamic snake convolutions, an eight-direction Mamba scan, and multi-scale edge fusion to achieve reportedly state-of-the-art Dice scores on DRIVE, STARE, and CHASE DB1.

  7. BadScan: An Architectural Backdoor Attack on Visual State Space Models

    cs.CV 2024-11 reject novelty 4.0 of 10

    BadScan is a trigger-activated architectural backdoor for VMamba that replaces the standard 2D selective scan with malformed scans at inference time.

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