期刊
REMOTE SENSING
卷 13, 期 16, 页码 -出版社
MDPI
DOI: 10.3390/rs13163122
关键词
water-body detection; multiscale residual network (MSResNet); self-supervised learning (SSL); high-resolution remote sensing imagery
类别
资金
- National Natural Science Foundation of China [41971284]
- State Key Program of the National Natural Science Foundation of China [42030102, 92038301]
- Foundation for Innovative Research Groups of the Natural Science Foundation of Hubei Province [2020CFA003]
This study introduces a novel multiscale residual network (MSResNet) that utilizes self-supervised learning (SSL) for water-body detection. The research addresses the shortcomings of traditional methods in capturing multiscale and multishape characteristics of water bodies, and effectively utilizes unlabeled data to enhance model performance.
Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.
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