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Urban areas extraction from multi sensor data based on machine learning and data fusion

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Abstract

Accurate urban areas information is important for a variety of applications, especially city planning and natural disaster prediction and management. In recent years, extraction of urban structures from remotely sensed images has been extensively explored. The key advantages of this imaging modality are reduction of surveying expense and time. It also elevates restrictions on ground surveys. Thus far, much research typically extracts these structures from very high resolution satellite imagery, which are unfortunately of relatively poor spectral resolution, resulting in good precision yet moderate accuracy. Therefore, this paper investigates extraction of buildings from middle and high resolution satellite images by using spectral indices (Normalized Difference Building Index: NDBI, Normalized Difference Vegetation Index: NDVI, Soil Adjustment Vegetation Index: SAVI, Modified Normalized Difference Index: MNDWI, and Global Environment Monitoring Index: GEMI) by means of various Machine Learning methods (Artificial Neural Network: ANN, K-Nearest Neighbor: KNN, and Support Vector Machine: SVM) and Data Fusion (i.e., Majority Voting). Herein empirical results suggested that suitable learning methods for urban areas extraction are in preferring order Data Fusion, SVM, KNN, and ANN. Their accuracies were 85.46, 84.86, 84.66, and 84.91%, respectively.

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Authors and Affiliations

  1. Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Сampus, Surat Thani, Thailand

    S. Puttinaovarat

  2. School of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

    P. Horkaew

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  1. S. Puttinaovarat
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Correspondence to S. Puttinaovarat.

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Supattra Puttinaovarat was born in 1983. Received her B.B.A. (Second Class Honors) in Business Computer and M.S. degree in Management of Information Technology from Prince of Songkla University, Thailand in 2007 and 2010, respectively. She is a lecturer at Faculty of Science and Industrial Technology, Prince of Songkla University, Suratthani Campus, Thailand. Her research interest includes Flood Modeling and Geographic Information System. She has published 10 scientific peer reviewed journal and conference paper.

Paramate Horkaew was born in 1978. Received his B.Eng. (First Class Honors) in Telecommunication Engineering (1999) from King Mongkut’s Institute of Technology, Ladkrabang, Thailand and Ph.D. in Computer Science from Imperial College London, University of London, London, United Kingdom (2004). He is an assistant professor at the School of Computer Engineering, Suranaree University of Technology, Thailand. His main research interests include Computational Anatomy, Digital Geometry Processing, Computer Vision and Graphics. He is the author of more than 30 peer reviewed journal and conference.

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Puttinaovarat, S., Horkaew, P. Urban areas extraction from multi sensor data based on machine learning and data fusion. Pattern Recognit. Image Anal. 27, 326–337 (2017). https://doi.org/10.1134/S1054661816040131

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