LKAFFNet: A Novel Large-Kernel Attention Feature Fusion Network for Land Cover Segmentation
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View abstract on PubMed
Summary
This summary is machine-generated.A new framework, LKAFFNet, improves land cover segmentation in remote sensing. It effectively balances local details and contextual information, outperforming existing models on benchmark datasets.
Area Of Science
- Remote Sensing
- Computer Vision
- Artificial Intelligence
Background
- Accurate land cover segmentation is vital for urban planning, environmental monitoring, and disaster management.
- Traditional Convolutional Neural Networks (CNNs) face challenges in integrating local details with large-scale context in high-resolution imagery.
Purpose Of The Study
- To develop a novel framework, LKAFFNet, that enhances land cover segmentation by addressing the limitations of traditional CNNs.
- To improve the balance between fine-grained local features and broad contextual information in remote sensing image analysis.
Main Methods
- Introduced LKAFFNet, a framework combining large-kernel convolutions, attention mechanisms, and multi-scale feature fusion.
- Developed three key modules: LkResNet for enhanced feature extraction with large-kernel convolutions, Large-Kernel Attention Aggregation (LKAA) for integrated spatial and channel attention, and Channel Difference Features Shift Fusion (CDFSF) for efficient multi-scale fusion.
Main Results
- LKAFFNet demonstrated superior performance compared to previous models on the LandCover and WHU Building datasets.
- Achieved a mean Intersection over Union (mIoU) of 0.8155 on the LandCover dataset and 0.9326 on the WHU Building dataset.
- The framework showed particular effectiveness in segmenting land cover across diverse scales.
Conclusions
- LKAFFNet significantly advances land cover segmentation accuracy in high-resolution remote sensing imagery.
- The proposed framework offers a more effective tool for various remote sensing applications requiring precise land cover classification.
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