Fang_Deep-Learning-Based_Multispectral_Satellite_Image_Segmentation_for_Water_Body_Detection.pdf (7.7 MB)
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Deep learning-based multi-spectral satellite image segmentation for water body detection

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journal contribution
posted on 24.09.2021, 12:53 by Kunhao Yuan, Xu Zhuang, Gerald SchaeferGerald Schaefer, Jianxin Feng, Lin GuanLin Guan, Hui FangHui Fang
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 multi-spectral imagery for such studies. However, how to effectively explore the wider spectrum bands of multi-spectral sensors to achieve significantly better performance compared to the use of only RGB bands has been left underexplored. In this paper, we propose a novel deep convolutional neural network model – Multi-Channel Water Body Detection Network (MC-WBDN) – that incorporates three innovative components, a multi-channel fusion module, an Enhanced Atrous Spatial Pyramid Pooling (EASPP) module, and Space-to-Depth (S2D)/Depth-to-Space (D2S) operations, to outperform state-of-the-art DCNN-based water body detection methods. Experimental results convincingly show that our MCWBDN 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.

Funding

Government of Chengdu City

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

14

Pages

7422-7434

Publisher

Institute of Electrical and Electronics Engineers

Version

VoR (Version of Record)

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

17/07/2021

Publication date

2021-07-21

Copyright date

2021

ISSN

1939-1404

eISSN

2151-1535

Language

en

Depositor

Dr Hui Fang. Deposit date: 17 July 2021