Machine learning for water bodies identification from satellite images. (2018)

• Record Type: Journal Article
• Authors: Kontos, Konstantinos; Maragoudakis, Manolis
• Is Part Of: International journal of data mining, modelling and management. Volume 10:Number 3(2018)
• Page Start: 209
• Page End: 228
• Publication Date: 2018
• Subjects: satellite images -- feature extraction -- image processing -- pool detection -- trainable segmentation -- data mining -- SVM algorithms -- decision trees -- image classification -- image mining -- AdaBoost; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

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.