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Photogrammetric processing algorithms can suffer problems due to either the initial image quality (noise, low radiometric quality,\udshadows and so on) or to certain surface materials (shiny or textureless objects). This can result in noisy point clouds and/or\uddifficulties in feature extraction. Specifically, dense point clouds which are generated with photogrammetric method using a\udlightweight thermal camera, are more noisy and sparse than the point clouds of high-resolution digital camera images. In this paper,\udnew method which produces more reliable and dense thermal point cloud using the sparse thermal point cloud and high resolution\uddigital point cloud was considered. Both thermal and digital images were obtained with UAS (Unmanned Aerial System) based\udlightweight Optris PI 450 and Canon EOS 605D camera images. Thermal and digital point clouds, and orthophotos were produced\udusing photogrammetric methods. Problematic thermal point cloud was transformed to a high density thermal point cloud using image\udprocessing methods such as rasterizing, registering, interpolation and filling. The results showed that the obtained thermal point\udcloud - up to chosen processing parameters - was 87% more densify than the original point cloud. The second improvement was\udgained at the height accuracy of the thermal point cloud. New densified point cloud has more consistent elevation model while the\udoriginal thermal point cloud shows serious deviations from the expected surface model

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

CORRECTION AND DENSIFICATION OF UAS-BASED PHOTOGRAMMETRIC THERMAL POINT CLOUD