Paper
27 September 2024 Improved mask-RCNN remote sensing image extraction based on attention mechanism
Yi Shi, Bokang Zhang
Author Affiliations +
Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328117 (2024) https://doi.org/10.1117/12.3050704
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China
Abstract
With the global climate change and the intensification of human activities, the frequency and the degree of damage of flood disasters have significantly increased. In order to prevent and control flood and waterlogging effectively, it is very important to obtain water area information timely and accurately. Remote sensing technology can provide a wide range of real-time water monitoring data, but in the complex water boundary identification and change detection, the traditional methods still have limitations. In this paper, a method of water remote sensing image extraction based on improved Mask-RCNN network is proposed, and its application in flood disaster prevention and control is explored. By introducing the attention mechanism of CBAM, the Mask-RCNN network is optimized, and the precision and efficiency of water area recognition are greatly improved. The experimental results show that the improved model is superior to the traditional U-Net model in mIoU, Recall and Precision, which provides technical support for flood disaster monitoring and early warning.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yi Shi and Bokang Zhang "Improved mask-RCNN remote sensing image extraction based on attention mechanism", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328117 (27 September 2024); https://doi.org/10.1117/12.3050704
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KEYWORDS
Performance modeling

Education and training

Remote sensing

Data modeling

Image segmentation

Mathematical optimization

Floods

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