Large-scale urban mapping using integrated geographic object-based image analysis and artificial bee colony optimization from worldview-3 data

Abstract

ABSTRACT Geographic object-based image analysis (GEOBIA) has demonstrated strong capability compared with pixel-based algorithms for urban characterization studies. In this study, new solutions to feature selection (FS) and image segmentation optimization are investigated in the GEOBIA domain. First, the combination of Taguchi-based optimization technique and F-score segmentation quality measures was adopted to optimize the parameters of multiresolution segmentation (MRS) and determine the optimum multiscale combinations of MRS parameters. Second, artificial bee colony (ABC) FS was integrated to select the most relevant features. Third, random forest (RF) classification algorithm was utilized to extract multiscale urban land use/land cover (LULC) classes from geographically wide images obtained from two WorldView-3 image datasets. The proposed method was developed in the first study area and later applied to the second study area for validation. Results of image segmentation optimization indicated that scales 40 and 80 were the best for classification. The result of FS through ABC outperformed those of other FS techniques, including support vector machine with recursive feature elimination (SVM-REF), variable selection using RF, Boruta, genetic algorithm, correlation-based FS, and chi-square, with an overall accuracy (OA) of 88.46%. Among the 100 examined features, only 25 were significant. The RF classification results showed a kappa coefficient (κ) of 0.84 in the first study area. The transferability and scalability of the best-performing features based on ABC FS were evaluated in the second study area, which covered a geographically wide scene of 162. The results for the second study area obtained an OA of 86.78% and a κ of 0.82. The proposed integrated method is an efficient and promising technique for high-quality LULC mapping of geographically wide areas.