Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method. Issue 10 (26th July 2020)

• Record Type: Journal Article
• Authors: Bramhe, Vijendra Singh; Ghosh, Sanjay Kumar; Garg, Pradeep Kumar
• Is Part Of: Geocarto international. Volume 35:Issue 10(2020)
• Page Start: 1067
• Page End: 1087
• Publication Date: 2020-07-26
• Subjects: Feature selection -- built-up area (BUA) extraction -- GLCM -- support vector machine -- artificial neural network; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2019.1566406
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k -Nearest Neighbour ( k -NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k -NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively.