MAFMamba: A Multi-Scale Adaptive Fusion Network for Semantic Segmentation of High-Resolution Remote Sensing Images
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View abstract on PubMed
Summary
This summary is machine-generated.MAFMamba, a novel visual Mamba network, enhances semantic segmentation for high-resolution remote sensing images by effectively handling scale variations and balancing local details with global context.
Area Of Science
- Computer Vision
- Remote Sensing
- Artificial Intelligence
Background
- High-resolution remote sensing imagery is crucial for geospatial information acquisition.
- Current semantic segmentation models struggle with scale variations and preserving fine details.
- Challenges include blurred boundaries, loss of small objects, and modeling diverse object appearances.
Purpose Of The Study
- To introduce MAFMamba, a multi-scale adaptive fusion visual Mamba network for high-resolution remote sensing image segmentation.
- To address limitations in capturing global context and local details, and modeling extreme scale variations.
- To improve semantic segmentation accuracy and efficiency in complex remote sensing scenes.
Main Methods
- Developed a lightweight hybrid encoder with an Adaptive Multi-scale Mamba Block (AMMB) and Multi-scale Adaptive Fusion (MSAF) for robust multi-scale representation.
- Introduced a Global-Local Feature Enhancement Mamba (GLMamba) in the decoder to balance convolutional local features with VSS global dependencies.
- Proposed a Multi-Scale Cross-Attention Fusion (MSCAF) module to bridge semantic gaps between encoder and decoder features.
Main Results
- MAFMamba outperformed state-of-the-art CNN, Transformer, and Mamba-based methods on ISPRS Potsdam and Vaihingen datasets in mIoU and mF1 scores.
- The network achieved superior accuracy in semantic segmentation of high-resolution remote sensing images.
- Demonstrated linear computational complexity and low memory usage, indicating high efficiency.
Conclusions
- MAFMamba effectively addresses scale variation and the trade-off between local details and global semantics in remote sensing image segmentation.
- The proposed architecture offers a significant advancement for accurate and efficient geospatial information acquisition.
- MAFMamba presents a promising solution for complex remote sensing scenarios requiring precise object delineation.
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