Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome th...

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Autores principales:	Jing Ling, Hongsheng Zhang, Yinyi Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optical and SAR fusion clouds urban land cover Science Q
Acceso en línea:	https://doaj.org/article/e51bacd91e01496aa1f5a276becbe1c4
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id	oai:doaj.org-article:e51bacd91e01496aa1f5a276becbe1c4
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spelling	oai:doaj.org-article:e51bacd91e01496aa1f5a276becbe1c42021-11-25T18:55:38ZImproving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images10.3390/rs132247082072-4292https://doaj.org/article/e51bacd91e01496aa1f5a276becbe1c42021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4708https://doaj.org/toc/2072-4292Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.Jing LingHongsheng ZhangYinyi LinMDPI AGarticleoptical and SAR fusioncloudsurban land coverScienceQENRemote Sensing, Vol 13, Iss 4708, p 4708 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optical and SAR fusion clouds urban land cover Science Q
spellingShingle	optical and SAR fusion clouds urban land cover Science Q Jing Ling Hongsheng Zhang Yinyi Lin Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
description	Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.
format	article
author	Jing Ling Hongsheng Zhang Yinyi Lin
author_facet	Jing Ling Hongsheng Zhang Yinyi Lin
author_sort	Jing Ling
title	Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
title_short	Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
title_full	Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
title_fullStr	Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
title_full_unstemmed	Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images
title_sort	improving urban land cover classification in cloud-prone areas with polarimetric sar images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e51bacd91e01496aa1f5a276becbe1c4
work_keys_str_mv	AT jingling improvingurbanlandcoverclassificationincloudproneareaswithpolarimetricsarimages AT hongshengzhang improvingurbanlandcoverclassificationincloudproneareaswithpolarimetricsarimages AT yinyilin improvingurbanlandcoverclassificationincloudproneareaswithpolarimetricsarimages
_version_	1718410560712736768

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

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