Water identification using convolutional neural networks

What is it about?

Precise monitoring of floods is significant in disaster management and loss reduction; however, remote sensing data resource and methods can largely affect the monitoring accuracy of flooded areas. In this study, we use cloud-free Sentinel-1 Synthetic Aperture Radar (SAR) imagery, preferable to the optical imagery. We have used 5 convolutional neural networks (CNNs), including HRNet, DenseNet, SegNet, ResNet and DeepLab v3 + for flood monitoring in the Poyang Lake area, and compared their performances with the traditional methods — the bimodal threshold segmentation (BTS) and the OSTU method. The HRNet has superior performance in water body identification with the highest precision and efficiency, based on a parallel structure to not only extract rich semantic information but also maintain high-resolution features in the whole process. Besides, speckle noise reduction by deep convolutional neural networks in SAR imagery is better compared with the Refined Lee filter. The CNNs are then used to monitor the temporal evolution of summer flooding (May-Nov.) in 2020. Results show the smallest water coverage of Poyang Lake in late May; it gradually increases to the maximum in mid-July, and then shows a downward trend until November.

Featured Image

Photo by Justin Wilkens on Unsplash

Photo by Justin Wilkens on Unsplash

Why is it important?

Precise water identification from remote sensing imagery is essential for mapping the water extent during flood events. Computer vision in deep learning has made strides in this regard. Convolutional neural network are used in this work to map the flood extent of the Poyang Lake area of China in 2020. The results are promising for flood disaster monitoring and mitigation.

Perspectives