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

dc.contributor.authorLópez, Alfonso
dc.contributor.authorJurado, Juan M.
dc.contributor.authorOgayar, Carlos J.
dc.contributor.authorFeito, Francisco R.
dc.date.accessioned2024-02-29T09:14:41Z
dc.date.available2024-02-29T09:14:41Z
dc.date.issued2021-12
dc.description.abstractThermal 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.es_ES
dc.identifier.otherhttps://doi.org/10.1016/j.isprsjprs.2021.09.022es_ES
dc.identifier.urihttps://hdl.handle.net/10953/2511
dc.publisherELSEVIERes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectUAV imageryes_ES
dc.subjectThermal imageryes_ES
dc.subjectImage processinges_ES
dc.subjectPoint cloudes_ES
dc.titleAn optimized approach for generating dense thermal point clouds from UAV-imageryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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