Ahmad, A., Hashim, U.K.M., Abd Wahid, R. et al. (5 more authors) (2019) Implementation of land cover change detection based on supervised classifications of multispectral satellite data for leveraging internet of things. ARPN Journal of Engineering and Applied Sciences, 14 (10). pp. 1863-1870. ISSN 1819-6608
Abstract
The study reported in this paper aims to detect land cover changes using multispectral and multitemporal remote sensing data. The data came from Landsat TM satellite covering the area of Klang, located in Selangor, Malaysia. Initially, pre-processing was carried out to identify the stability of three supervised methods namely maximum likelihood (ML), neural network (NN) and support vector machines (SVM) as the size of training pixels changed For this purpose, Landsat bands 1, 2, 3, 4, 5 and 7 for the year 1998 were used as the input for each of these methods to classify land covers within the study area. The generated land cover classifications were evaluated by statistically comparing each land cover with a reference data set using a confusion matrix. Subsequently, these methods were used to classify land covers of the same area using Landsat data acquired in the year 2000 and 2005. The 2005 classification was then statistically compared with the 2000 classification using a confusion matrix for each of the methods. This produced land cover changes that occurred between 2000 and 2005 which were generated using SVM, ML and NN. Results showed that land cover change detection using SVM was quantitatively and qualitatively more accurate compared to ML and NN mainly due to the least affected by the size of training pixels. The findings of the study are relevant and beneficial in leveraging the internet of things practices.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Asian Research Publishing Network (ARPN). |
Keywords: | land cover; change detection; remote sensing; multispectral; classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Sep 2019 09:00 |
Last Modified: | 24 Sep 2019 09:05 |
Published Version: | http://www.arpnjournals.com/jeas/volume_10_2019.ht... |
Status: | Published |
Publisher: | Asian Research Publishing Network (ARPN) |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151197 |