An optimized approach for generating dense thermal point clouds from UAV-imagery

• Published in: ISPRS journal of photogrammetry and remote sensing 2021-12, Vol.182, p.78-95
• Main Authors: López, Alfonso, Jurado, Juan M., Ogayar, Carlos J., Feito, Francisco R.
• Format: Article
• Language: English
• Subjects: GPU computing; Image processing; Occlusion; Point cloud; Thermal imagery; UAV imagery
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2021.09.022

[Display omitted] •Reconstruction of large point clouds with thermal data through an automatic method.•Minimization of the distance from aggregated thermal values to image information.•Massively parallel computing with GPU hardware for time-consuming tasks.•Thermal characterization of vegetation by detecting ground points. Thermal infrared (TIR) images acquired from Unmanned Aircraft Vehicles (UAV) are gaining scientific interest in a wide variety of fields. However, the reconstruction of three-dimensional (3D) point clouds utilizing consumer-grade TIR images presents multiple drawbacks as a consequence of low-resolution and induced aberrations. Consequently, these problems may lead photogrammetric techniques, such as Structure from Motion (SfM), to generate poor results. This work proposes the use of RGB point clouds estimated from SfM as the input for building thermal point clouds. For that purpose, RGB and thermal imagery are registered using the Enhanced Correlation Coefficient (ECC) algorithm after removing acquisition errors, thus allowing us to project TIR images into an RGB point cloud. Furthermore, we consider several methods to provide accurate thermal values for each 3D point. First, the occlusion problem is solved through two different approaches, so that points that are not visible from a viewing angle do not erroneously receive values from foreground objects. Then, we propose a flexible method to aggregate multiple thermal values considering the dispersion from such aggregation to the image samples. Therefore, it minimizes error measurements. A naive classification algorithm is then applied to the thermal point clouds as a case study for evaluating the temperature of vegetation and ground points. As a result, our approach builds thermal point clouds with up to 798,69% more point density than results from other commercial solutions. Moreover, it minimizes the build time by using parallel computing for time-consuming tasks. Despite obtaining larger point clouds, we report up to 96,73% less processing time per 3D point.