Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas

Abstract

Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas.