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| Title | Lkaffnet: A Novel Large-Kernel Attention Feature Fusion Network For Land Cover Segmentation |
|---|---|
| ID_Doc | 35401 |
| Authors | Chen B.; Tong A.; Wang Y.; Zhang J.; Yang X.; Im S.-K. |
| Year | 2025 |
| Published | Sensors, 25, 1 |
| DOI | http://dx.doi.org/10.3390/s25010054 |
| Abstract | 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. © 2024 by the authors. |
| Author Keywords | CNN; deep learning; feature restoration; smart city; sustainable building; urban land use |
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