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A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images

Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surfa...

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Autores principales: Wang, Mingchang, Ding, Wen, Wang, Fengyan, Song, Yulian, Chen, Xueye, Liu, Ziwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147273/
https://www.ncbi.nlm.nih.gov/pubmed/35632332
http://dx.doi.org/10.3390/s22103924
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author Wang, Mingchang
Ding, Wen
Wang, Fengyan
Song, Yulian
Chen, Xueye
Liu, Ziwei
author_facet Wang, Mingchang
Ding, Wen
Wang, Fengyan
Song, Yulian
Chen, Xueye
Liu, Ziwei
author_sort Wang, Mingchang
collection PubMed
description Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality.
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spelling pubmed-91472732022-05-29 A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images Wang, Mingchang Ding, Wen Wang, Fengyan Song, Yulian Chen, Xueye Liu, Ziwei Sensors (Basel) Article Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality. MDPI 2022-05-22 /pmc/articles/PMC9147273/ /pubmed/35632332 http://dx.doi.org/10.3390/s22103924 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Mingchang
Ding, Wen
Wang, Fengyan
Song, Yulian
Chen, Xueye
Liu, Ziwei
A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title_full A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title_fullStr A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title_full_unstemmed A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title_short A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images
title_sort novel bayes approach to impervious surface extraction from high-resolution remote sensing images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147273/
https://www.ncbi.nlm.nih.gov/pubmed/35632332
http://dx.doi.org/10.3390/s22103924
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