A Deep Learning Method of Water Body Extraction From High Resolution Remote Sensing Images With Multi-sensors

Mengya Li, Penghai Wu, Biao Wang, Honglyun Park, Yang Hui, Wu Yanlan
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images. However, these methods are usually aimed at a specific sensor and are not applicable. Thus, we proposed a new network, called the dense-local-feature-compression (DLFC) network aiming at extracting water body from different remote sensing images automatic. In this network, each layer of the network can receive the feature
more » ... ps of all layers before it by the densely connected module of DenseNet. The concatenate operation on the feature dimension is used when connecting across layers. It can realize the different levels of features reuse. The local-feature-compression module is introduced before concatenate operation. It can obtain the more abstract features further by the convolution operation. Through the DLFC, we can fuse the spatial and spectral information for the remote sensing images that can extract water body from different remote sensing images. Besides, we construct a new water body dataset based on GaoFen-2 (GF-2) remote sensing images. The proposed DLFC achieved excellent performance with GF-2, GaoFen-6, Sentinel-2, and ZY-3 remote sensing images. Compared with the traditional water body extraction method and contemporary networks, the DLFC exhibits noticeable improvement. The results indicate that the DLFC can realize water body extraction from multisource remote sensing images automatically and rapidly. Index Terms-Deep learning, high resolution, multisource remote sensing images, water body. His research interests include remote sensing image semantic segmentation, artificial intelligence, and geographic information mining. Yanlan Wu received the Ph.D. degree in cartography and geographic information system from Wuhan University,
doi:10.1109/jstars.2021.3060769 fatcat:bpl746mtejbataudaxbjmkvznm