Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Hamedianfar, Alireza; Gibril, Mohamed Barakat A.; Hosseinpoor, Mohammadjavad; Pellikka, Petri K. E.
• Is Part Of: Geocarto international. Volume 37:Issue 3(2022)
• Page Start: 773
• Page End: 791
• Publication Date: 2022-02-01
• Subjects: Artificial neural network -- particle swarm optimization -- extreme gradient boosting classifier -- feature selection -- WorldView-3 -- urban area; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1737974
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data.