Abstract
Automated water body detection from satellite imagery is a fundamental stage for urban hydrological studies. In recent years, various deep convolutional neural network (DCNN)-based methods have been proposed to segment remote sensing data collected by conventional RGB or multispectral imagery for such studies. However, how to effectively explore the wider spectrum bands of multispectral sensors to achieve significantly better performance compared to the use of only RGB bands has been left underexplored. In this article, we propose a novel DCNN model-multichannel water body detection network (MC-WBDN)-that incorporates three innovative components, i.e., a multichannel fusion module, an Enhanced Atrous Spatial Pyramid Pooling module, and Space-to-Depth/Depth-to-Space operations, to outperform state-of-the-art DCNN-based water body detection methods. Experimental results convincingly show that our MC-WBDN model achieves remarkable water body detection performance, is more robust to light and weather variations, and can better distinguish tiny water bodies compared to other DCNN models.
Highlights
W ATER body detection from remote sensing imagery is of great importance for urban hydrological studies [1]
We propose a novel Multi-Channel Water Body Detection Network (MC-WBDN) that exploits the potential of multi-spectral imagery to improve the performance of stateof-the-art deep convolutional neural network (DCNN) models for water body segmentation
Motivated by the success of deep learning methods and their applications to remote sensing, in this paper, we have introduced a novel approach to satellite-based water body extraction, accomplished through an effective deep convolutional neural network that incorporates several contributions
Summary
W ATER body detection from remote sensing imagery is of great importance for urban hydrological studies [1]. Urban hydrology has become an emerging research area that allows to improve and manage urban water systems for solving environmental issues caused by rapid urbanisation It facilitates timely flood protection planning and water quality control for public safety and health [2]. Since its launch in 2015, the Sentinel-2 satellite has provided publicly available multi-spectral imagery that has been widely employed in land-cover applications [5], [6], [7] It offers one of the most suitable data sources for timely urban hydrological monitoring and analysis due to its neardaily update frequency compared to higher-resolution remote sensing data such as Very High Spatial Resolution (VHR) [8] and Synthetic Aperture Radar (SAR) [4]. In this paper, we investigate the use of 10 meter resolution multi-spectral data from Sentinel-2 due to its potential for urban hydrological applications that require frequently updated data in their analysis process
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