Title: Machine learning for water bodies identification from satellite images

Authors: Konstantinos Kontos; Manolis Maragoudakis

Addresses: Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece ' Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece

Abstract: Examining satellite images on residential areas and more particularly bodies of water such as swimming pools are of great interest in the field of image mining. Initially, the unobstructed water consumption for pool operation can lead to the reduction of water supplies especially during summer months, a fact that can influence water sources for firefighting. Moreover, they may serve as potential mosquito habitat, especially if they are surrounded by dense vegetation. Towards this direction, this paper presents an efficient classification system for identifying swimming pools from satellite images. A new method of trainable segmentation is presented for feature extraction. In this study, a support vector machine algorithm is used for reducing the feature set to the more appropriate one. The proposed method was tested on different areas of Greece with an overall accuracy of 99.82% that was achieved by using an ensemble algorithm.

Keywords: satellite images; feature extraction; image processing; pool detection; trainable segmentation; data mining; SVM algorithms; decision trees; image classification; image mining; AdaBoost.

DOI: 10.1504/IJDMMM.2018.093881

International Journal of Data Mining, Modelling and Management, 2018 Vol.10 No.3, pp.209 - 228

Available online: 30 Jul 2018 *

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