Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve the positional accuracy of the resulting orthomosaic images and 3D models. Software improvements to reduce these errors will be especially useful if delivered in an open-source product. Python code was developed to preprocess the images that were input to OpenDroneMap (ODM). The system was tested on a variety of different datasets that each contained a subset of the characteristics that often cause problems (movement, reflection, or undifferentiated areas). Various combinations of filters (treatments) were applied to the datasets and the 2D and 3D results were reviewed for a reduction in artifacts. The results were significantly better with respect to artifacts, but no significant improvement in positional accuracy was observed except in the cases where the drone stopped when capturing an image.
","abstract_html":"<p>Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve the positional accuracy of the resulting orthomosaic images and 3D models. Software improvements to reduce these errors will be especially useful if delivered in an open-source product. Python code was developed to preprocess the images that were input to OpenDroneMap (ODM). The system was tested on a variety of different datasets that each contained a subset of the characteristics that often cause problems (movement, reflection, or undifferentiated areas). Various combinations of filters (treatments) were applied to the datasets and the 2D and 3D results were reviewed for a reduction in artifacts. The results were significantly better with respect to artifacts, but no significant improvement in positional accuracy was observed except in the cases where the drone stopped when capturing an image. <strong></strong></p>","abstract_has_math":false,"creators":["Ironsmith, Eddie"],"institution":null,"degree_name":"Doctor of Philosophy - Forestry","degree_level":"Dissertation","degree_discipline":"Forestry","degree_department":null,"school":null,"contributors":["Dr. Yanli Zhang","Dr. I-Kuai Hung","Dr. David Kulhavy"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-13T08:00:00Z","date_published":"2024-12-13T08:00:00Z","updated_at":"2026-07-24T04:30:45Z","subjects":["UAS 3D Model Orthophoto Artifacts Registration","Other Computer Sciences","Other Forestry and Forest Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/582","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Yanli Zhang","Dr. I-Kuai Hung","Dr. David Kulhavy"]},{"key":"dc:creator","label":"Author","values":["Ironsmith, Eddie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-03T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Forestry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy - Forestry"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["UAS 3D Model Orthophoto Artifacts Registration","Other Computer Sciences","Other Forestry and Forest Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.sfasu.edu/etds/582"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve the positional accuracy of the resulting orthomosaic images and 3D models. Software improvements to reduce these errors will be especially useful if delivered in an open-source product. Python code was developed to preprocess the images that were input to OpenDroneMap (ODM). The system was tested on a variety of different datasets that each contained a subset of the characteristics that often cause problems (movement, reflection, or undifferentiated areas). Various combinations of filters (treatments) were applied to the datasets and the 2D and 3D results were reviewed for a reduction in artifacts. The results were significantly better with respect to artifacts, but no significant improvement in positional accuracy was observed except in the cases where the drone stopped when capturing an image.
"]},{"key":"dc:title","label":"Title","values":["UNMANNED AERIAL SYSTEMS (UAS) IMAGE PREPROCESSING TO REDUCE ARTIFACTS AND IMPROVE GEOMETRIC REGISTRATION WHEN GENERATING ORTHOPHOTO MOSAICS AND 3D MODELS"]}]}],"canonical_facts":{"dc:contributor":["Dr. Yanli Zhang","Dr. I-Kuai Hung","Dr. David Kulhavy"],"dc:creator":["Ironsmith, Eddie"],"dc:date.available":["2025-01-03T08:00:00Z"],"dc:description.abstract":["Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve the positional accuracy of the resulting orthomosaic images and 3D models. Software improvements to reduce these errors will be especially useful if delivered in an open-source product. Python code was developed to preprocess the images that were input to OpenDroneMap (ODM). The system was tested on a variety of different datasets that each contained a subset of the characteristics that often cause problems (movement, reflection, or undifferentiated areas). Various combinations of filters (treatments) were applied to the datasets and the 2D and 3D results were reviewed for a reduction in artifacts. The results were significantly better with respect to artifacts, but no significant improvement in positional accuracy was observed except in the cases where the drone stopped when capturing an image.
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/582"],"dc:subject":["UAS 3D Model Orthophoto Artifacts Registration","Other Computer Sciences","Other Forestry and Forest Sciences"],"dc:title":["UNMANNED AERIAL SYSTEMS (UAS) IMAGE PREPROCESSING TO REDUCE ARTIFACTS AND IMPROVE GEOMETRIC REGISTRATION WHEN GENERATING ORTHOPHOTO MOSAICS AND 3D MODELS"],"thesis:degree_discipline":["Forestry"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy - Forestry"]},"updated_at":"2026-07-24T04:30:45Z"}