Polymodal Method of Improving the Quality of Photogrammetric Images and Models

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.

Methodology for Combining Data Acquired by Unmanned Surface and Aerial Vehicles to Create Digital Bathymetric Models in Shallow and Ultra-Shallow Waters

“…It was also proved in [6] that the quality of the bathymetric SfM is highly sensitive to flow, turbidity, and color. Other factors can also affect the development of any photogrammetric products, such as specific atmospheric conditions affecting the quality of the photo radiometry [16]; blurs appearing on the images [17]; water surface reflections [18]; and sea state, sunglint, and solar elevation angles [19]. In [20], refraction issues are additionally raised.…”

Seven Different Lighting Conditions in Photogrammetric Studies of a 3D Urban Mock-Up

“…The software is one of the most popular programs used in photogrammetry (mainly in short-range photogrammetry) [53]. Aside from standard functionalities (photogrammetric triangulation [27], digital surface/digital terrain model generation [54,55], georeferenced orthomosaic generation [56,57], 3D model generation and texturing [58]), it enables the use of Python and Java APIs, which allow for adapting the software to the user's needs, often dictated by its use for specialist analyses by scientists in the photogrammetry and teledetection fields [23,59,60].…”

“…The results of this work can be the basis for planning the distribution of F-points when it is impossible to manipulate additional lighting (as in the case of studies conducted with the purpose of monitoring external artificial light at night-ALAN) or to select or change the artificial lighting of objects constituting the subject of the study. It must be noted that the quality of a photogrammetric study is affected by many factors, the specific division of which can be found in paper [23]. The most important factors include: object shape and colour [24,25], image quality [26], used software, methods [22,27], used sensors [28], accuracy of support systems [29][30][31][32] and data fusion methods [33][34][35], sensor orientation [1] or even the sensor's temperature [36].…”

Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality

“…The radiometric quality of the images is affected by many factors such as exposure parameters and sensor properties, flight parameters, and external conditions [8]. There are three groups of factors that influence the quality of images and the whole photogrammetric process: procedures, technical elements, and numerical methods [9]. Procedures refer to parameters related to imagery data collection.…”

“…Platform vibrations (often caused by poor vibroisolation and lack of stabilizing gimbals) can cause image blur and a jello effect [16-Figure 1. A breakdown of factors influencing UAV image quality (based on [9]).…”