Improved Classification Accuracy Based on the Output-Level Fusion of High-Resolution Satellite Images and Airborne LiDAR Data in Urban Area

Abstract

This letter proposes a method based on the fusion of high-resolution satellite images and airborne light detection and ranging (LiDAR) data for improving classification accuracy. Based on output-level fusion during classification, the proposed method utilizes a three-step process to minimize the misclassification of buildings and road objects. First, elevated road areas are detected in ground points, which are extracted for the generation of a digital terrain model based on statistical values. Second, building information is extracted from a satellite image through the output-level fusion of various data results. Third, supervised classification is conducted using a support vector machine for areas that lack elevated roads and buildings. We evaluated the proposed method by comparing it with a pixel-based method and analyzing experimental WorldView-2 images and airborne LiDAR data. We conducted a visual interpretation and quantitative accuracy assessment. The overall accuracy and kappa coefficient of the proposed method were 90.91% and 0.892, respectively. These results demonstrated an improvement in the overall accuracy and kappa coefficient by 11.27 percentage points and 0.135, respectively, compared with the pixel-based method. The results confirmed that our proposed method has significant potential for classifying urban environments using high-resolution satellite imagery and airborne LiDAR data.