Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is stil...

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Main Authors: Amir Tan, Adhwa, Mohd Shafri, Helmi Zulhaidi, Shaharum, Nur Shafira Nisa
Format: Article
Published: College of Graduate Studies of Walailak University 2021
Online Access:http://psasir.upm.edu.my/id/eprint/94547/
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author Amir Tan, Adhwa
Mohd Shafri, Helmi Zulhaidi
Shaharum, Nur Shafira Nisa
author_facet Amir Tan, Adhwa
Mohd Shafri, Helmi Zulhaidi
Shaharum, Nur Shafira Nisa
author_sort Amir Tan, Adhwa
building UPM Institutional Repository
collection Online Access
description Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications.
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spelling upm-945472022-12-04T23:55:15Z http://psasir.upm.edu.my/id/eprint/94547/ Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods Amir Tan, Adhwa Mohd Shafri, Helmi Zulhaidi Shaharum, Nur Shafira Nisa Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. College of Graduate Studies of Walailak University 2021-10 Article PeerReviewed Amir Tan, Adhwa and Mohd Shafri, Helmi Zulhaidi and Shaharum, Nur Shafira Nisa (2021) Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods. Trends in Sciences, 18 (21). pp. 1-15. ISSN 2774-0226 https://tis.wu.ac.th/index.php/tis/article/view/38 10.48048/tis.2021.38
spellingShingle Amir Tan, Adhwa
Mohd Shafri, Helmi Zulhaidi
Shaharum, Nur Shafira Nisa
Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title_full Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title_fullStr Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title_full_unstemmed Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title_short Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods
title_sort performance of sentinel-2a remote sensing system for urban area mapping in malaysia via pixel-based and obia methods
url http://psasir.upm.edu.my/id/eprint/94547/
http://psasir.upm.edu.my/id/eprint/94547/
http://psasir.upm.edu.my/id/eprint/94547/