Extracting surface water bodies from sentinel-2 imagery using convolutional neural networks

Abstract

Water is an integral part of eco-system with significant role in human life. It is immensely mobilized natural resource and hence it should be monitored continuously. Water features extracted from satellite images can be utilized for urban planning, disaster management, geospatial dataset update and similar other applications. In this research, surface water features from Sentinel-2 (S2) images were extracted using state-of-the-art approaches of deep learning. Performance of three proposed networks from different research were assessed along with baseline model. In addition, two existing but novel architects of Convolutional Neural Network (CNN) namely; Densely Convolutional Network (DenseNet) and Residual Attention Network (AttResNet) were also implemented to make comparative study of all the networks. Then dense blocks, transition blocks, attention block and residual block were integrated to propose a novel network for water bodies extraction. Talking about existing networks, our experiments suggested that DenseNet was the best network among them with highest test accuracy and recall values for water and non water across all the experimented patch sizes. DenseNet achieved the test accuracy of 89.73% with recall values 85 and 92 for water and non water respectively at the patch size of 16. Then our proposed network surpassed the performance of DenseNet by reaching the test accuracy of 90.29% and recall values 86 and 93 for water and non water respectively. Moreover, our experiments verified that neural network were better than index-based approaches since the index-based approaches did not perform well to extract riverbanks, small water bodies and dried rivers. Qualitative analysis seconded the findings of quantitative analysis. It was found that the proposed network was successful in creating attention aware features of water pixels and diminishing urban, barren and non water pixels. All in all, it was concluded that the objectives of the research were met successfully with the successful proposition of a new network.