Use this URL to cite or link to this record in EThOS:
Title: Impact of image quality on SfM photogrammetry : colour, compression and noise
Author: O'Connor, James
ISNI:       0000 0004 8500 7307
Awarding Body: Kingston University
Current Institution: Kingston University
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Structure-from-motion (SfM) photogrammetry has become ubiquitous in the geosciences, owing to its low-cost and ease of use for generating 3D data. Ideas around data collection, quality and processing need to be revisited to ensure that the technology is being harnessed correctly. One area which is new in this current image acquisition boom is the range of sensors and systems being used to collect image data. This raises crucial questions in the geoscience community which are addressed in this contribution. This is split into three parts. Firstly, image quality is investigated to establish whether a stable association between it and the quality of photogrammetric products can be uncovered to allow simpler and more effective inter-comparison of results between studies. This was accomplished by artificially degrading a very high-quality benchmark dataset of a coastal cliff and a landslide in Norfolk, UK. Results revealed that the level of noise, image compression and downsampling all degrade the quality of 3D products from the SfM workflow. Secondly, these sets of images were pre-processed to establish whether results could be augmented by controlling the single colour channel used during photogrammetric processing. Results showed slight variations in the products generated, with evidence supporting the fine sensitivity SfM has for refining the focal length estimation of the lens. For extremely specific contexts, pre-processing of the RGB-to-single channel conversion may be relevant, but for the datasets analysed in this contribution this was not the case. Thirdly, image network configurations were investigated to build on previous research in establishing best practice. Results show that, in situations where the number of images being acquired is a limiting factor, networks with narrowly oblique overlapping images have a higher density and lower error than those with widely oblique images and those directly facing the surface normal. These results demonstrate the value of optimising image acquisition, and in the handling of this imagery. The differences in image quality and pre-processing which are unreported within geoscientific studies using SfM could account for differences between accuracies obtained, independent of the specific photogrammetric methods used. Insights from this work into how best to capture, process and produce the best quality SfM data will allow the community to adopt these best practices in the future.
Supervisor: Smith, Mike ; Walford, Nigel ; James, Mike R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: UAV ; photogrammetry ; remote sensing ; structure from motion ; image processing ; computer vision