Visual recognition memory for scenes in aerial photographs: Exploring the role of expertise

Šikl, Radovan; Svatoňová, Hana; Děchtěrenko, Filip; Urbánek, Tomáš

doi:10.1016/j.actpsy.2019.04.019

Cited by 8 publications

(9 citation statements)

References 49 publications

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“…Knowledge of expertise in remote sensing surveying at the OS is based in large part on the workplace observation that experience correlates with better performance in domain-specific visual tasks. The underlying processes of learning for aerial images have only been investigated in a small number of studies ( Borders, Dennis, Noesen, & Harel, 2020 ; Lansdale et al 2010 ; Lloyd, Hodgson, & Stokes, 2002 ; Šikl et al, 2019 ). Šikl et al (2019) found that remote sensing experts were better at recognizing aerial-view scenes from memory than novices.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies of visual expertise include work on medical imagery (e.g., Fox & Faulkner-Jones, 2017 ; Gegenfurtner, Lehtinen, & Säljö, 2011 ; Krupinski, 2010 ; Krupinski et al, 2006 ; Reingold & Sheridan, 2012 ; Wolfe, Evans, Drew, Aizenman, & Josephs, 2016 ), pilot perception in the aviation cockpit (e.g., Bellenkes, Wickens, & Kramer, 1997 ; Kasarskis, Stehwien, Hickox, Aretz, & Wickens, 2001 ; Schriver, Morrow, Wickens, & Talleur, 2008 ; Ziv, 2016 ) and 2D aerial imagery analysis ( Lansdale, Underwood, & Davies, 2010 ; Šikl, Svatoňová, Děchtěrenko, & Urbánek, 2019 ). Performance metrics tend to show that experts require less time, have higher accuracy and produce fewer errors than novices in detection and classification tasks within their domain of expertise.…”

Section: Introductionmentioning

confidence: 99%

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Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

Skog,

Meese,

Sargent

et al. 2024

Journal of Vision

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Using a novel approach to classification images (CIs), we investigated the visual expertise of surveyors for luminance and binocular disparity cues simultaneously after screening for stereoacuity. Stereoscopic aerial images of hedges and ditches were classified in 10,000 trials by six trained remote sensing surveyors and six novices. Images were heavily masked with luminance and disparity noise simultaneously. Hedge and ditch images had reversed disparity on around half the trials meaning hedges became ditch-like and vice versa. The hedge and ditch images were also flipped vertically on around half the trials, changing the direction of the light source and completing a 2 × 2 × 2 stimulus design. CIs were generated by accumulating the noise textures associated with “hedge” and “ditch” classifications, respectively, and subtracting one from the other. Typical CIs had a central peak with one or two negative side-lobes. We found clear differences in the amplitudes and shapes of perceptual templates across groups and noise-type, with experts prioritizing binocular disparity and using this more effectively. Contrariwise, novices used luminance cues more than experts meaning that task motivation alone could not explain group differences. Asymmetries in the luminance CIs revealed individual differences for lighting interpretation, with experts less prone to assume lighting from above, consistent with their training on aerial images of UK scenes lit by a southerly sun. Our results show that (i) dual noise in images can be used to produce simultaneous CI pairs, (ii) expertise for disparity cues does not depend on stereoacuity, (iii) CIs reveal the visual strategies developed by experts, (iv) top-down perceptual biases can be overcome with long-term learning effects, and (v) CIs have practical potential for directing visual training.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

Skog,

Meese,

Sargent

et al. 2024

Journal of Vision

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“…One clear example of a domain that requires expertise in scene recognition is analysis of aerial scene imagery (Lloyd, Hodgson, & Stokes, 2002;Šikl et al, 2019). Recognizing aerial scenes presents unique challenges to the human visual system.…”

Section: Experience On Scene Recognitionmentioning

confidence: 99%

Seeing the World from Above: Training with Aerial Scenes Reveals the Effects of Experience on Scene Recognition

Borders¹,

Dennis²,

Noesen³

et al. 2020

Preprint

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Throughout evolution, the human visual system has adapted to efficiently encode several environmental constants to deal with the huge complexity involved in representing large-scale spatial environments. Being terrestrial animals, these constants reflect a specific ground-based viewpoint. For example, people show a strong affinity for detecting a perceptual upright layout relative to the horizon. But, what happens when humans leave this terrestrial perspective, for example when taking a flight, or looking down from the Empire State building? Are the mechanisms originally evolved for terrestrial scene recognition also recruited for the recognition of novel large-scale environments people rarely encounter on a daily basis? We propose that studying how people learn to recognize aerial scene images can reveal how the scene recognition system develops through experience and shed light on the putative mechanisms underlying its malleability. We conducted an intensive six-session behavioral training study in which naive participants learned to categorize manmade and natural scenes at a specific-subordinate level (‘suspension bridge’). Scene images depicted real-world places from a terrestrial and an aerial viewpoint, allowing us to establish how people learn to categorize two visually-different images as the same environment. We found that performance constantly improved over the first five training sessions, with greater learning for the aerial compared to the terrestrial scenes. This viewpoint performance gap manifested more for manmade than natural scenes. In addition to memory improvements, we also found learning transfer, the hallmark of perceptual learning. Performance in the sixth session (in which participants categorized scenes they had not been trained with) was significantly better compared to the first session, and equivalent to average performance across training-sessions. Our findings provide novel evidence for the potential mechanisms underlying plasticity in the scene recognition system, showing both memory and perception contribute to experience-based enhancements in scene recognition.

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“…Recently, many studies have confirmed the effectiveness of eye tracking and offered valid results, such as the differences between expert and novice groups when assessing the biodiversity of harvested forests (Pihel et al, 2015 ), the visual perception discrepancies between experts and laymen when viewing landscape photos (Dupont et al, 2015 ), the disparity in visual recognition memory between expert image analysts and untrained viewers when observing aerial photographs (Šikl et al, 2019 ), and the visual differences between the potential Chinese and English rural tourists' landscape preferences (Ren, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Similarities and Differences in the Outsiders and Insiders' Visual Preferences on Sacred Landscape

Wang

2022

Front. Psychol.

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Previous studies have reported religious and non-religious people as having different psychological experiences when visiting sacred landscapes; however, the visual consensus and differences between diverse groups visiting them have rarely been considered. This study used subjective preference evaluation and experimental eye tracking to assess the visual preferences of different groups regarding sacred landscapes. Overall, 48 photos of the Han Chinese Buddhist temples were selected as stimulus materials, including the categories of squares, architecture, waterscapes, and plants. In all, 90 participants were classified into two groups of outsiders and insiders to view the photos. The consensus and differences in their visual preferences and eye movement metrics were evaluated. The results showed that the two groups were more inclined toward the visual preference of religious architectures than the natural landscape that people usually prefer. Another noteworthy discovery revealed the significant differences between the outsiders and the insiders in viewing and evaluating sacred landscapes; the immersion effect explains this result. Specifically, the group with a higher interaction with the environment had greater visual experiences, easier visual information coding, and larger visual exploration range. In addition, this study revealed familiarity with the religious background facilitated achieving a higher consistency between the landscape preference scores and the eye movement metrics. These findings expand the theory of religious environment perception and provided important insights for subsequent research on sacred landscape planning and management.

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Visual recognition memory for scenes in aerial photographs: Exploring the role of expertise

Cited by 8 publications

References 49 publications

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above

Seeing the World from Above: Training with Aerial Scenes Reveals the Effects of Experience on Scene Recognition

Similarities and Differences in the Outsiders and Insiders' Visual Preferences on Sacred Landscape

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