Extracting built-up areas from spectro-textural information using machine learning
Article
Article Title | Extracting built-up areas from spectro-textural information using machine learning |
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ERA Journal ID | 36486 |
Article Category | Article |
Authors | Maqsoom, Ahsen (Author), Aslam, Bilal (Author), Yousafzai, Arbaz (Author), Ullah, Fahim (Author), Ullah, Sami (Author) and Imran, Muhammad (Author) |
Journal Title | Soft Computing |
Journal Citation | 26 (16), pp. 7789-7808 |
Number of Pages | 20 |
Year | 2022 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1432-7643 |
1433-7479 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-022-06794-6 |
Web Address (URL) | https://link.springer.com/article/10.1007/s00500-022-06794-6 |
Abstract | Extraction of built-up area (BUA) is essential for proper city planning and management. It enables the concerned authorities to formulate better city development policies and manage emergent disasters. However, the traditionally used optical data present spectral confusion where BUAs are mixed with other features adding to management complexities. Therefore, an advanced automated method is required to extract the spectral and textural features from satellite data for the pattern recognition of BUA. Landsat-8 Operational Land Imager (OLI) has been used in the current study to identify the pattern and extract BUA of Gujranwala, Pakistan. First, eight textural features based on the gray-level co-occurrence matrix (GLCM) are selected and combined with multispectral data. Then, feature selection methods are applied to select optimal features used to train the proposed support vector machine (SVM) classifier. Finally, the results from SVM classifiers are compared with k-nearest neighbor (k-NN) and backpropagation neural network (BP-NN) to highlight any improvements in results. The comparisons show that the proposed approach increases the overall accuracy of linear-SVM by 8.41%, radial basis function SVM by 8.3%, BP-NN by 7.63%, and k-NN by 6.6%. This can help city managers and planners to extract critical BUA information in otherwise unplanned and rapidly expanding cities to move toward smart and sustainable cities. |
Keywords | built-up areas; support vector machine; spectro-textural information; pattern recognition; city planning |
ANZSRC Field of Research 2020 | 339999. Other built environment and design not elsewhere classified |
469999. Other information and computing sciences not elsewhere classified | |
330408. Strategic, metropolitan and regional planning | |
330201. Automation and technology in building and construction | |
460199. Applied computing not elsewhere classified | |
401302. Geospatial information systems and geospatial data modelling | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | COMSATS University Islamabad, Pakistan |
Quaid-i-Azam University, Pakistan | |
School of Surveying and Built Environment | |
Lahore Development Authority, Pakistan | |
Federation University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7133/extracting-built-up-areas-from-spectro-textural-information-using-machine-learning
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