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October 17, 2025Remote SensingOpen Access

Semantic Segmentation of High-Resolution Remote Sensing Images Based on RS3Mamba: An Investigation of the Extraction Algorithm for Rural Compound Utilization Status

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Authors

XFXinyu FangZLZhenbo LiuSXShangbin Xie

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Overview

This investigation demonstrates improved feature extraction methods using RS3Mamba for rural compounds, suggesting enhanced image analysis capabilities.

Key Points

  • Extraction accuracy achieved an average intersection over union of 79.64%, improving upon existing models.
  • The RS3Mamba deep learning model effectively captures long-range spatial correlations in rural compounds.
  • A multiscale attention feature fusion mechanism significantly enhances edge contour extraction in courtyards.
  • The model mitigates false alarms caused by shadows and complex textures, showcasing its practical significance.

Cite This Study

Fang et al. (2025) studied this question.

synapsesocial.com/papers/68f19f1ade32064e504dda36https://doi.org/10.3390/rs17203443
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Semantic Segmentation of High-Resolution Remote Sensing Images Based on RS³Mamba: An Investigation on the Extraction Algorithm of Rural Compound Utilization Status2025 · 1 citations
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  3. 3RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation2024 · 5 citations
  4. 4MSS-MambaNet: A Mamba Framework for Building Extraction from Multi-Phase Disaster Imagery2026
  5. 5DSD-Mamba: Dual-Stream Semantic Segmentation of Remote Sensing Imagery via Dense-Sparse Fusion2026