A novel NDSM fusion approach to improve UAV-Based LCC accuracy in sloping urban areas

Abstract

Offering rapidly and periodically achievable and very high resolution (VHR) data at low cost, unmanned aerial vehicles (UAVs) have recently become one of the most popular remote sensing technologies. In this study, a novel Normalized Digital Surface Model (NDSM) fusion approach is proposed to improve the object-based land cover classification (LCC) performance of the RGB camera equipped UAVs. The UAV flights were completed in a multi-class sloping urban area with oblique viewing geometry and a VHR orthomosaic was generated. The object-based LCC was performed applying bottom-up (BU) multiresolution segmentation and Nearest Neighbor and Rule-Based classification algorithms. In order to improve the limited segmentation performance of the RGB orthomosaic, high-quality NDSM was produced and fused as an additional imaging band. The highest LCC accuracy utilizing four imaging bands were achieved with a weight of 0.3 for NDSM and 1 for RGB bands. To preserve the information provided by the NDSM data for each image object, a single-stage segmentation process has been performed in contrast to BU segmentation. The results demonstrated that the proposed approach increased the LCC precision up to 15%, recall up to 4.71%, F1 score up to 8.58%, Kappa as 7% and overall accuracy as 4.57%.