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June 14, 2026International Journal of Remote Sensing

SS-MambaUnet for fault segmentation in multi-source remote sensing images

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Authors

XWXinshuo WangLLL S Li

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Overview

Randomized trial demonstrates improved fault segmentation in remote sensing images, suggesting enhanced model efficiency.

Key Points

  • The aim is to develop a model that effectively segments faults in multi-source remote sensing images.
  • Developed SS-MambaUnet using an enhanced HSVfusion method for data integration.
  • Embedded Band Attention Module for adaptive feature selection.
  • Introduced Spectral Mamba and Spatial Mamba modules for long-range dependency capture.
  • SS-MambaUnet achieved a mean Intersection over Union (mIoU) of 88.23%.
  • Outperformed U-Net, DeepLabV3+, and Swin-Unet by 8.71%, 7.54%, and 3.65% points, respectively.
  • Surpassed recent Mamba-based models by an average of 1.42% points.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a2e44e4b1cc60ccdea8a37fhttps://doi.org/10.1080/01431161.2026.2684249
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Also Consider

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  1. 1UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images2024 · 74 citations
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  3. 3LMVMamba: A Hybrid U-Shape Mamba for Remote Sensing Segmentation with Adaptation Fine-Tuning2025 · 3 citations
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  5. 5MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification2024 · 1 citations