New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images. Issue 2 (17th January 2017)

• Record Type: Journal Article
• Authors: Gibril, Mohamed Barakat A.; Shafri, Helmi Z. M.; Hamedianfar, Alireza
• Is Part Of: International journal of remote sensing. Volume 38:Issue 2(2017)
• Page Start: 467
• Page End: 491
• Publication Date: 2017-01-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1266109
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The use of asbestos cement (AC) roofing materials is a significant concern because of their deleterious effects on human health and the environment. The main objective of this study was to map AC roofs from WorldView-2 (WV-2) images using object-based image analysis (OBIA). A robust Taguchi optimization technique was used to optimize segmentation parameters for WV-2 images in heterogeneous urban areas. In this research, two subsets of WV-2 satellite image sets were utilized to map AC roofs. Rule-based OBIA framework was developed on the first study area. Different supervised OBIA classifiers, such as Bayes, k -nearest neighbour ( k -NN), support vector machine (SVM), and random forest (RF), were tested on the first image of the study areas to evaluate the performance of a rule-based classifier. Results of the supervised classifiers showed confusion between AC roof class and some urban features, with overall accuracies of 72.21%, 77%, 81.75%, and 82.02% for Bayes, k -NN, SVM, and RF, respectively. To assess the transferability of the proposed method, the adopted classification framework was applied to larger subsets of WV-2 of the second study area. The results of the proposed approach showed outstanding performance, with overall accuracies of 93.10% and 90.74% for the first and second classified images, respectively. The McNemar test emphasized the statistical reliability of rule-based result (in the first site) compared with supervised classification results. Therefore, the proposed framework of using rule-based classification and Taguchi optimization technique provide an efficient and expeditious approach to mapping and monitoring the presence of AC roofs and help local authorities in their decision-making strategies and policies.