Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784716767131598848 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9147273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wangmingchang anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT dingwen anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT wangfengyan anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT songyulian anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT chenxueye anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT liuziwei anovelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT wangmingchang novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT dingwen novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT wangfengyan novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT songyulian novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT chenxueye novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages AT liuziwei novelbayesapproachtoimpervioussurfaceextractionfromhighresolutionremotesensingimages |