Rhodes Rebecca E, Cowley Hannah P, Huang Jay G, Gray-Roncal William, Wester Brock A, Drenkow Nathan
Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States.
Front Psychol. 2021 Dec 14;12:733021. doi: 10.3389/fpsyg.2021.733021. eCollection 2021.
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.
航空图像在地理空间分析中经常被用于为应对危机和灾害提供信息，但当它们包含低分辨率、颜色信息退化以及物体尺寸较小时，可能会给视觉搜索带来独特的挑战。航空图像分析通常由人工进行，但机器学习方法也在不断发展以辅助人工分析。然而，迄今为止，相对较少的工作探讨了人类如何在这些任务上进行视觉搜索，而了解这一点最终可能有助于实现人机协作。我们设计了一系列研究，以了解航空图像的哪些特征会使人类的视觉搜索变得困难，以及人类在执行这些任务时会使用哪些策略。在两项实验中，我们用一系列航空图像测试了人类在计数任务上的表现，并研究了目标大小、位置、颜色、清晰度和目标数量等特征对准确性和搜索策略的影响。两项实验都呈现了由航空卫星图像组成的试验；要求参与者在图像中找到搜索模板的所有实例。目标大小一直是表现的重要预测指标，不仅影响选择的准确性，还影响参与者在试验中选择目标实例的顺序。实验2表明，目标实例的清晰度以及搜索模板颜色与目标实例颜色之间的匹配度也能预测准确性。此外，颜色还能预测试验中选择实例的顺序。这些实验不仅确立了人类在航空图像视觉搜索方面的典型表现基准，还识别出了几个可能影响人类任务难度水平的特征。这些结果对于理解人类在现实世界任务中的视觉搜索以及人类何时可能从自动化方法中受益具有重要意义。
