Water Body Extraction from SAR and Multi-Source Data Using Siamese Network-Based Segmentation

Ting Liu, Mengke Yuan, Chaoran Lu, Kaixuan Lu, Baochai Peng, Heyang Duan, Mengya Li, Pan Zhang, Tao Wang, Tongkui Liao

Abstract: Extracting water bodies using C-band synthetic aperture radar (SAR) images has high reliability. However, the limited backscatter sensitivity of SAR data makes it difficult to differentiate between water and other features such as roads, building shadows, and more. To tackle these issues, we have implemented several strategies: Firstly, we have augmented our input data by incorporating polarized VV and VH data, as well as additional modalities such as DTM, DSM, and land use information. Through rigorous ablation studies, we have validated the efficacy of these data modalities, even in the presence of potential inaccuracies. Secondly, we have constructed a Siamese network for feature extraction and fusion across multiple modalities. Experimental results reveal that our proposed framework surpasses traditional feature extraction methods, yielding a 0.8% increase in F1 score. Finally, we have employed techniques such as stochastic multi-scale training, two-stage training, and model ensemble to further enhance performance. Notably, our method secured the second position in the 2024 IEEE GRSS Data Fusion Contest (DFC) Track 1 test phase, achieving an F1 score of 79.170%.