1. A noise-robust water segmentation method based on synthetic aperture radar images combined with automatic sample collection.
- Author
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Hou, Zhuoyan, Meng, Mengmeng, Zhou, Guichao, Zhang, Xuedong, Cao, Mingjun, Qian, Junhao, Li, Ning, Huang, Yabo, Wu, Lin, and Xie, Linglin
- Subjects
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SYNTHETIC aperture radar , *BODIES of water , *SYNTHETIC apertures , *SPECKLE interference , *AUTOMATIC identification , *WATER sampling , *WATER use - Abstract
Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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