Water extraction model of multispectral optical remote sensing image

Accurate monitoring of surface water is an important basic application of remote sensing. The principle of optical remote sensing water extraction is based on different ground features having different spectral reflection characteristics. Some ground features (ice, snow, shadows, clouds) have simila...

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Autores principales:	DENG Kaiyuan, REN Chao
Formato:	article
Lenguaje:	ZH
Publicado:	Surveying and Mapping Press 2021
Materias:	remote sensing optical remote sensing image water index water resources water extract Mathematical geography. Cartography GA1-1776
Acceso en línea:	https://doaj.org/article/32659bc7fee04d5ba070abf1de52d6a2
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spelling	oai:doaj.org-article:32659bc7fee04d5ba070abf1de52d6a22021-11-12T02:25:59ZWater extraction model of multispectral optical remote sensing image1001-159510.11947/j.AGCS.2021.20200482https://doaj.org/article/32659bc7fee04d5ba070abf1de52d6a22021-10-01T00:00:00Zhttp://xb.sinomaps.com/article/2021/1001-1595/2021-10-1370.htmhttps://doaj.org/toc/1001-1595Accurate monitoring of surface water is an important basic application of remote sensing. The principle of optical remote sensing water extraction is based on different ground features having different spectral reflection characteristics. Some ground features (ice, snow, shadows, clouds) have similar reflection characteristics to water bodies, which leads to the failure of extraction and classification. Aiming at the problem of misclassification and omission of traditional water body index in water body extraction, this paper proposes a normalized difference multi-band water index model. This paper uses two experiments to test the stability of the new index. The area of experiment 1 is the Linzhi area of Tibet. The data source is Landsat 8 and Sentinel 2 satellite images in the same time phase. The experimental results verify the ability of the new index to suppress snow and ice. The Kappa coefficient of the new index is 0.86, the overall accuracy is 0.93, and the misclassification error is 0.03, the omission error is 0.12, the drawing accuracy is 0.97, and the producer accuracy is 0.88, which are better than the existing index. The data source of experiment 2 was GF-1, and Hong Kong Disneyland was used as the experimental area. Experiment 3 extracted water bodies in multiple regions and verified the stability of the water body index in this paper. Water extraction was performed in an environment with a small amount of clouds, which proved that the new index can suppress clouds and their shadows. This paper uses multi-source optical remote sensing image to verify the feasibility of the new index. Without additional auxiliary data, the influence of snow, cloud and shadow can be eliminated, and the water can be more effectively and automatically extracted, which can be extended to coastal resource research, glacier change, inland lake change and other fields.DENG KaiyuanREN ChaoSurveying and Mapping Pressarticleremote sensingoptical remote sensing imagewater indexwater resourceswater extractMathematical geography. CartographyGA1-1776ZHActa Geodaetica et Cartographica Sinica, Vol 50, Iss 10, Pp 1370-1379 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	remote sensing optical remote sensing image water index water resources water extract Mathematical geography. Cartography GA1-1776
spellingShingle	remote sensing optical remote sensing image water index water resources water extract Mathematical geography. Cartography GA1-1776 DENG Kaiyuan REN Chao Water extraction model of multispectral optical remote sensing image
description	Accurate monitoring of surface water is an important basic application of remote sensing. The principle of optical remote sensing water extraction is based on different ground features having different spectral reflection characteristics. Some ground features (ice, snow, shadows, clouds) have similar reflection characteristics to water bodies, which leads to the failure of extraction and classification. Aiming at the problem of misclassification and omission of traditional water body index in water body extraction, this paper proposes a normalized difference multi-band water index model. This paper uses two experiments to test the stability of the new index. The area of experiment 1 is the Linzhi area of Tibet. The data source is Landsat 8 and Sentinel 2 satellite images in the same time phase. The experimental results verify the ability of the new index to suppress snow and ice. The Kappa coefficient of the new index is 0.86, the overall accuracy is 0.93, and the misclassification error is 0.03, the omission error is 0.12, the drawing accuracy is 0.97, and the producer accuracy is 0.88, which are better than the existing index. The data source of experiment 2 was GF-1, and Hong Kong Disneyland was used as the experimental area. Experiment 3 extracted water bodies in multiple regions and verified the stability of the water body index in this paper. Water extraction was performed in an environment with a small amount of clouds, which proved that the new index can suppress clouds and their shadows. This paper uses multi-source optical remote sensing image to verify the feasibility of the new index. Without additional auxiliary data, the influence of snow, cloud and shadow can be eliminated, and the water can be more effectively and automatically extracted, which can be extended to coastal resource research, glacier change, inland lake change and other fields.
format	article
author	DENG Kaiyuan REN Chao
author_facet	DENG Kaiyuan REN Chao
author_sort	DENG Kaiyuan
title	Water extraction model of multispectral optical remote sensing image
title_short	Water extraction model of multispectral optical remote sensing image
title_full	Water extraction model of multispectral optical remote sensing image
title_fullStr	Water extraction model of multispectral optical remote sensing image
title_full_unstemmed	Water extraction model of multispectral optical remote sensing image
title_sort	water extraction model of multispectral optical remote sensing image
publisher	Surveying and Mapping Press
publishDate	2021
url	https://doaj.org/article/32659bc7fee04d5ba070abf1de52d6a2
work_keys_str_mv	AT dengkaiyuan waterextractionmodelofmultispectralopticalremotesensingimage AT renchao waterextractionmodelofmultispectralopticalremotesensingimage
_version_	1718431282207129600

Water extraction model of multispectral optical remote sensing image

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