Wang, R.; Zhang, C.; Chen, C.; Hao, H.; Li, W.; Jiao, L. A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing. Remote Sens.2024, 16, 419.
Wang, R.; Zhang, C.; Chen, C.; Hao, H.; Li, W.; Jiao, L. A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing. Remote Sens. 2024, 16, 419.
Wang, R.; Zhang, C.; Chen, C.; Hao, H.; Li, W.; Jiao, L. A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing. Remote Sens.2024, 16, 419.
Wang, R.; Zhang, C.; Chen, C.; Hao, H.; Li, W.; Jiao, L. A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing. Remote Sens. 2024, 16, 419.
Abstract
Water area segmentation in remote sensing is of great importance for flood monitoring. To
overcome some challenges in this task, we construct the Water Index and Polarization Information
(WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net
(MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and
reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively
mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot
handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception
(GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by
introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-
Inception to further improve model performance. Extensive experiments on three benchmarks,
including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable
performance with lower complexity and better robustness against six competing approaches. For
example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of
0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the
U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have
been released on GitHub
Keywords
water area segmentation; multi-modality fusion; multi-filter inception; attention mechanism; remote sensing
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
