Abstract
U-Net has been widely applied in semantic segmentation tasks, but it faces challenges in the semantic segmentation of high-resolution remote sensing images due to the loss of boundary information during the downsampling process and the inherent blurriness of object boundaries in remote sensing images. We propose an advanced U-Net variant model that addresses these issues. By introducing the CBAM attention mechanism, we enhance the extraction of boundary information during the downsampling process, and by incorporating a cascaded edge detection module, we significantly improve the model's boundary segmentation performance. As a result, the model demonstrates excellent performance in the segmentation of high-resolution remote sensing images. The results indicate that our proposed model outperforms other baseline models and exhibits superior performance.
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Cao, X., Qin, J. (2024). CCU-NET: CBAM and Cascaded Edge Detection Optimization U-NET for Remote Sensing Image Segmentation. In: Xu, C., et al. Data Science. ICPCSEE 2024. Communications in Computer and Information Science, vol 2215. Springer, Singapore. https://doi.org/10.1007/978-981-97-8749-4_12
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DOI: https://doi.org/10.1007/978-981-97-8749-4_12
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