LKAFFNet: A Novel Large-Kernel Attention Feature Fusion Network for Land Cover Segmentation
The accurate segmentation of land cover in high-resolution remote sensing imagery is crucial for applications such as urban planning, environmental monitoring, and disaster management. However, traditional convolutional neural networks (CNNs) struggle to balance fine-grained local detail with large-...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/54 |
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| Summary: | The accurate segmentation of land cover in high-resolution remote sensing imagery is crucial for applications such as urban planning, environmental monitoring, and disaster management. However, traditional convolutional neural networks (CNNs) struggle to balance fine-grained local detail with large-scale contextual information. To tackle these challenges, we combine large-kernel convolutions, attention mechanisms, and multi-scale feature fusion to form a novel LKAFFNet framework that introduces the following three key modules: LkResNet, which enhances feature extraction through parameterizable large-kernel convolutions; Large-Kernel Attention Aggregation (LKAA), integrating spatial and channel attention; and Channel Difference Features Shift Fusion (CDFSF), which enables efficient multi-scale feature fusion. Experimental comparisons demonstrate that LKAFFNet outperforms previous models on both the LandCover dataset and WHU Building dataset, particularly in cases with diverse scales. Specifically, it achieved a mIoU of 0.8155 on the LandCover dataset and 0.9326 on the WHU Building dataset. These findings suggest that LKAFFNet significantly improves land cover segmentation performance, offering a more effective tool for remote sensing applications. |
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| ISSN: | 1424-8220 |