research-article

NDWI-DeepLabv3+: High-Precision Extraction of Water Bodies from Remote Sensing Images

Authors:

Yuhao Peng,

HouCheng Su,

Chao Xu,

Ao Feng,

Tao LiuAuthors Info & Claims

MLMI '20: Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence

Pages 110 - 116

https://doi.org/10.1145/3426826.3426847

Published: 17 December 2020 Publication History

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Abstract

How to efficiently and accurately extract water bodies from remote sensing images is the focus of scholars' research. Current research often does not make full use of the unique multi-band data of remote sensing images. This paper proposes an improved NDWI-DeepLabv3+ network to improve the accuracy of water body extraction, especially from urban remote sensing images. We improve the network from two main aspects: multi-scale input and multi-band data feature fusion. And for the critical parts of the network, we put forward a variety of feasible solutions to compare and select the best. In the end, we chose to convert the feature map calculated by NDWI into an input adapted to the neural network, and at the same time, develop a parallel convolution structure to fuse and extract the band data features. We verify the effectiveness of this method by comparing other multi-scale architecture networks in the same period. The NDWI-DeepLabV3+ network proposed in this paper can extract water from the L2A level data of Sentinel-2, which can slightly increase the computational consumption and obtain better performance. It provides new ideas for intelligently extracting hydrological information from remote sensing images.

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Kubendiran IRamaiah M(2024)Modeling, Mapping and Analysis of Floods Using Optical, Lidar and SAR Datasets—a ReviewWater Resources10.1134/S009780782360061451:4(438-448)Online publication date: 10-Jul-2024
https://doi.org/10.1134/S0097807823600614
George BSajith VSowmya. VSivanpillai R(2023)Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN57001.2023.10116311(603-608)Online publication date: 23-Mar-2023
https://doi.org/10.1109/SPIN57001.2023.10116311
Zhao YCai DLyu XCheng D(2023)Terraced field extraction in UAV imagery using improved DeepLabv3+ network2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP58490.2023.10248552(854-859)Online publication date: 21-Apr-2023
https://doi.org/10.1109/ICSP58490.2023.10248552

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MLMI '20: Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence

September 2020

138 pages

ISBN:9781450388344

DOI:10.1145/3426826

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 December 2020

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MLMI '20: 2020 The 3rd International Conference on Machine Learning and Machine Intelligence

September 18 - 20, 2020

Hangzhou, China

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Kubendiran IRamaiah M(2024)Modeling, Mapping and Analysis of Floods Using Optical, Lidar and SAR Datasets—a ReviewWater Resources10.1134/S009780782360061451:4(438-448)Online publication date: 10-Jul-2024
https://doi.org/10.1134/S0097807823600614
George BSajith VSowmya. VSivanpillai R(2023)Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN57001.2023.10116311(603-608)Online publication date: 23-Mar-2023
https://doi.org/10.1109/SPIN57001.2023.10116311
Zhao YCai DLyu XCheng D(2023)Terraced field extraction in UAV imagery using improved DeepLabv3+ network2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP58490.2023.10248552(854-859)Online publication date: 21-Apr-2023
https://doi.org/10.1109/ICSP58490.2023.10248552

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A Robust Feature Extraction Method for Heterogenous Remote Sensing Images Based on Feature Transfer

Lake water body extraction of optical remote sensing images based on semantic segmentation

Multi-texture-Model for Water Extraction Based on Remote Sensing Image

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