Cargando…

Benchmarking Human Performance for Visual Search of Aerial Images

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but ma...

Descripción completa

Detalles Bibliográficos
Autores principales: Rhodes, Rebecca E., Cowley, Hannah P., Huang, Jay G., Gray-Roncal, William, Wester, Brock A., Drenkow, Nathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8713551/
https://www.ncbi.nlm.nih.gov/pubmed/34970183
http://dx.doi.org/10.3389/fpsyg.2021.733021
_version_ 1784623785554477056
author Rhodes, Rebecca E.
Cowley, Hannah P.
Huang, Jay G.
Gray-Roncal, William
Wester, Brock A.
Drenkow, Nathan
author_facet Rhodes, Rebecca E.
Cowley, Hannah P.
Huang, Jay G.
Gray-Roncal, William
Wester, Brock A.
Drenkow, Nathan
author_sort Rhodes, Rebecca E.
collection PubMed
description Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches.
format Online
Article
Text
id pubmed-8713551
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87135512021-12-29 Benchmarking Human Performance for Visual Search of Aerial Images Rhodes, Rebecca E. Cowley, Hannah P. Huang, Jay G. Gray-Roncal, William Wester, Brock A. Drenkow, Nathan Front Psychol Psychology Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches. Frontiers Media S.A. 2021-12-14 /pmc/articles/PMC8713551/ /pubmed/34970183 http://dx.doi.org/10.3389/fpsyg.2021.733021 Text en Copyright © 2021 Rhodes, Cowley, Huang, Gray-Roncal, Wester and Drenkow. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Rhodes, Rebecca E.
Cowley, Hannah P.
Huang, Jay G.
Gray-Roncal, William
Wester, Brock A.
Drenkow, Nathan
Benchmarking Human Performance for Visual Search of Aerial Images
title Benchmarking Human Performance for Visual Search of Aerial Images
title_full Benchmarking Human Performance for Visual Search of Aerial Images
title_fullStr Benchmarking Human Performance for Visual Search of Aerial Images
title_full_unstemmed Benchmarking Human Performance for Visual Search of Aerial Images
title_short Benchmarking Human Performance for Visual Search of Aerial Images
title_sort benchmarking human performance for visual search of aerial images
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8713551/
https://www.ncbi.nlm.nih.gov/pubmed/34970183
http://dx.doi.org/10.3389/fpsyg.2021.733021
work_keys_str_mv AT rhodesrebeccae benchmarkinghumanperformanceforvisualsearchofaerialimages
AT cowleyhannahp benchmarkinghumanperformanceforvisualsearchofaerialimages
AT huangjayg benchmarkinghumanperformanceforvisualsearchofaerialimages
AT grayroncalwilliam benchmarkinghumanperformanceforvisualsearchofaerialimages
AT westerbrocka benchmarkinghumanperformanceforvisualsearchofaerialimages
AT drenkownathan benchmarkinghumanperformanceforvisualsearchofaerialimages