Abstract
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.
Highlights
Photogrammetry using unmanned aerial vehicles, understood to be a tool for taking measurements, combines the possibility of ground, air and even suborbital photogrammetric measurements [1], at the same time being a low-cost competition for conventional aerial photogrammetry or satellite observation
The impact of three basic image quality-degrading factors on the processing in modern photogrammetry software and on the quality of models based on such images was assessed, a polymodal algorithm for improving measurement image quality based on neural networks and numerical methods was developed, image-degrading factors were eliminated, their quality was objectively assessed, and basic photogrammetric products were developed
The popular peak-signal-to-noise ratio ratio (PSNR) index indicated a significant improvement of image quality in all tasks, with the highest value observed for deblurring, for which blind referenceless image spatial quality evaluator (BRISQUE) and perception-based image quality evaluator (PIQE) showed quite the opposite
Summary
Photogrammetry using unmanned aerial vehicles, understood to be a tool for taking measurements, combines the possibility of ground, air and even suborbital photogrammetric measurements [1], at the same time being a low-cost competition for conventional aerial photogrammetry or satellite observation. In the event of UAV images containing typical quality-degrading elements, such as noise, blur, and low resolution, one can apply an additional process to eliminate these factors, improving the final quality of a photogrammetric product. This additional process interferes only with the image data directly prior to their processing, it does not change the elements of the software itself. The impact of three basic image quality-degrading factors (noise, blur, and low resolution) on the processing in modern photogrammetry software and on the quality of models based on such images was assessed, a polymodal algorithm for improving measurement image quality based on neural networks and numerical methods was developed, image-degrading factors were eliminated, their quality was objectively assessed, and basic photogrammetric products were developed. Models developed from the recovered images, which are images after elimination of degradation factors, were compared with a reference model
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