Polymodal Method of Improving the Quality of Photogrammetric Images and Models

Energies

Pawel Burdziakowski¹ 

[1] Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11-12, 80-233 Gdansk, Poland;

关键词: UAV;    neural networks;    deblur;    denoise;    super resolution;    neural network;   

DOI  :  10.3390/en14123457

来源: DOAJ

【 摘 要 】

Photogrammetry using unmanned aerial vehicles has become very popular and is already commonly used. The most frequent photogrammetry products are an orthoimage, digital terrain model and a 3D object model. When executing measurement flights, it may happen that there are unsuitable lighting conditions, and the flight itself is fast and not very stable. As a result, noise and blur appear on the images, and the images themselves can have too low of a resolution to satisfy the quality requirements for a photogrammetric product. In such cases, the obtained images are useless or will significantly reduce the quality of the end-product of low-level photogrammetry. A new polymodal method of improving measurement image quality has been proposed to avoid such issues. The method discussed in this article removes degrading factors from the images and, as a consequence, improves the geometric and interpretative quality of a photogrammetric product. The author analyzed 17 various image degradation cases, developed 34 models based on degraded and recovered images, and conducted an objective analysis of the quality of the recovered images and models. As evidenced, the result was a significant improvement in the interpretative quality of the images themselves and a better geometry model.

【 授权许可】

Unknown