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Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand

Author

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  • Niang Sian Lun

    (Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, Klong Luang, Pathum Thani 12120, Thailand)

  • Siddharth Chaudhary

    (Department of Biological Systems Engineering, Washington State University, Pullman, WA 99163, USA)

  • Sarawut Ninsawat

    (Remote Sensing and GIS, School of Engineering and Technology, Asian Institute of Technology, Klong Luang, Pathum Thani 12120, Thailand)

Abstract

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively.

Suggested Citation

  • Niang Sian Lun & Siddharth Chaudhary & Sarawut Ninsawat, 2023. "Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1051-:d:1026958
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    References listed on IDEAS

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    1. Chudech Losiri & Masahiko Nagai & Sarawut Ninsawat & Rajendra P. Shrestha, 2016. "Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models," Sustainability, MDPI, vol. 8(7), pages 1-23, July.
    2. Teerawate Limgomonvilas, 2017. "Prediction for Nonthaburi Urban Parks by Integrated Geo-Informatics Techniques," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(1), pages 20-28.
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    Cited by:

    1. Xue Shi & Yu Wang & Haotian You & Jianjun Chen, 2023. "Sea Ice Extraction in SAR Images via a Spatially Constrained Gamma Mixture Model," Sustainability, MDPI, vol. 15(13), pages 1-17, June.

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