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


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


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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Official URL or Download Paper: https://tis.wu.ac.th/index.php/tis/article/view/38

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.48048/tis.2021.38
Publisher: College of Graduate Studies of Walailak University
Keywords: Sentinel-2; Landsat-8; Urban mapping; Image classification; Object-based classification
Depositing User: Mas Norain Hashim
Date Deposited: 04 Dec 2022 23:55
Last Modified: 04 Dec 2022 23:55
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.48048/tis.2021.38
URI: http://psasir.upm.edu.my/id/eprint/94547
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