Precise 3D extraction of building roofs by fusion of UAV-based thermal and visible images

Thermography is an efficient way of detecting the thermal problems of the roof as a major part of a building's energy dissipation. Thermal images have a low spatial resolution, making it a challenge to produce a three-dimensional thermal model using aerial images. This paper proposes a combination of thermal and visible point clouds to generate a higher-resolution thermal point cloud from roofs of buildings. For this purpose, after obtaining the internal orientation parameters through camera calibration, visible and thermal point clouds were generated and then registered to each other using ground control points. The roofs of buildings were then extracted from the visible point cloud in four steps. First ground points were removed using cloth simulation filter (CSF), and then vegetation points were eliminated by applying entropy and red-green-blue vegetation index (RGBVI). Geometric features and a segmentation step were considered to filter roofs from other parts. Finally, by combining visible and thermal point clouds, the generated point had a high spatial resolution along with thermal information. In the achieved results, the thermal camera calibration was performed with an accuracy of 0.31 pixels, and the thermal and visible point clouds were registered with an absolute distance of < 0.3 m. The experimental results showed an accuracy of 18 cm for automated extraction of building roofs and 0.6 pixel for production of a high-resolution thermal point cloud, which was five times the density of the primary thermal point cloud and free from distortions.

Automated summary

This paper proposes a method that combines visible and thermal point clouds to generate a higher-resolution thermal point cloud and successfully extract building roofs from it, providing a new approach to detecting thermal issues.