Water body

Scale (ratio)

Boundary (topology)

Geotechnical engineering

Cartography

Computer science

Surface (topology)

Geology

Image resolution

Environmental engineering

Surface water

Pattern recognition (psychology)

Mathematics

Geography

Multispectral image

Environmental science

Mathematical analysis

Artificial intelligence

Geometry

Remote sensing

High resolution

Artificial neural network

Convolutional neural network

Convolution (computer science)

Journal of Hydrology

Surface water mapping is very important for studying its role in global water cycle, flooding dynamic monitoring, and water resources management. The most famous techniques for water body extraction like those based on water spectral indices (WSI) require rich spectral information. However, the WSI methods are no longer practical in high-resolution multispectral (MS) images due to insufficient spectral information. In addition, surface water mapping faces an utmost overestimation issue because shadows are misclassified as water bodies. To address the above-mentioned problems, in this paper, a novel refined water body extraction neural network (WBE-NN) is proposed. The global spatial-spectral convolution (GSSC) module is developed to enhance surface water body features. A novel multiscale learning module is designed to extract multi-scale contextual information. In addition, the surface water body boundary refinement (SWBBR) module is adopted to enhance surface water body boundaries. The results show that the proposed method achieved good performance with a mean overall accuracy of 98.97%, a mean Kappa coefficient of 94.78%, and a mean boundary overall accuracy of 98.01%. Therefore, WBE-NN can be used for mapping surface water with high accuracy in complex areas.

A novel water body extraction neural network (WBE-NN) for optical high-resolution multispectral imagery