Comparing experts and novices in Martian surface feature change detection and identification

Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided “expert” data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects

Real-world categories don't allow uniform feature spaces - not just across categories but within categories also [Open peer commentary on Schyns, P.G., Goldstone, R.L., & Thibaut, J. The development of features in object concepts] [Letter]

The Schyns et al. target article demonstrates that different classifications entail different representations, implying “flexible space learning.” We argue that flexibility is required even at the within-category level

Research and Technology

Johnson Space Center (JSC) accomplishments in new and advanced concepts during 1989 are highlighted. This year, reports are grouped in sections, Medical Science, Solar System Sciences, Space Transportation Technology, and Space Systems Technology. Summary sections describing the role of JSC in each program are followed by descriptions of significant tasks. Descriptions are suitable for external consumption, free of technical jargon, and illustrated to increase ease of comprehension

Johnson Space Center Research and Technology 1993 Annual Report

Johnson Space Center research and technology accomplishments during fiscal year 1993 are described and principle researchers and technologists are identified as contacts for further information. Each of the four sections gives a summary of overall progress in a major discipline, followed by detailed, illustrated descriptions of significant tasks. The four disciplines are Life Sciences, Human Support Technology, Solar Systems Sciences, and Space Systems Technology. The report is intended for technical and management audiences throughout the NASA and worldwide aerospace community. An index lists project titles, funding codes, and principal investigators

Accurate detection methods for GAN-generated earth observation images using expert visual perception

Image generation techniques, such as generative adversarial networks (GANs), have become sufficiently sophisticated to cause growing security concerns regarding image authenticity. Although generation and detection methods are often applied to a range of images such as objects and faces, more domain specific image types such as Earth Observation (EO) have received relatively little attention, leaving the field vulnerable to potential malicious misuse of this technology. This thesis investigates the current state of EO specific GAN generation and detection methods using an interdisciplinary approach. This work argues that further detection methods should incorporate both human and computational detection to improve current techniques. Evidence to support this conclusion is given by the following contributions: 1. A literature review of the current state of image generation and detection with respect to EO imagery. 2. A new benchmark evaluation of current GAN models in the task of the unconditional generation of synthetic EO imagery. 3. A Comparison between detection methods in both human and computer detection systems towards synthetic EO imagery that quantifies the key behavioural differences and effectiveness for each approach. The findings from two image detection studies show that these systems prioritize different image features for making accurate detections. 4. An eye-tracking image detection study between expert and novice users. The results find that experts exhibit more efficient and effective visual search strategies for detection. 5. The development of a novel framework to improve current techniques by guiding a CNN detection model using eye gaze data from self-reported high experience individuals. The results found that this approach increased detection performance over control models

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Accurate detection methods for GAN-generated earth observation images using expert visual perception

Image generation techniques, such as generative adversarial networks (GANs), have become sufficiently sophisticated to cause growing security concerns regarding image authenticity. Although generation and detection methods are often applied to a range of images such as objects and faces, more domain specific image types such as Earth Observation (EO) have received relatively little attention, leaving the field vulnerable to potential malicious misuse of this technology. This thesis investigates the current state of EO specific GAN generation and detection methods using an interdisciplinary approach. This work argues that further detection methods should incorporate both human and computational detection to improve current techniques. Evidence to support this conclusion is given by the following contributions: 1. A literature review of the current state of image generation and detection with respect to EO imagery. 2. A new benchmark evaluation of current GAN models in the task of the unconditional generation of synthetic EO imagery. 3. A Comparison between detection methods in both human and computer detection systems towards synthetic EO imagery that quantifies the key behavioural differences and effectiveness for each approach. The findings from two image detection studies show that these systems prioritize different image features for making accurate detections. 4. An eye-tracking image detection study between expert and novice users. The results find that experts exhibit more efficient and effective visual search strategies for detection. 5. The development of a novel framework to improve current techniques by guiding a CNN detection model using eye gaze data from self-reported high experience individuals. The results found that this approach increased detection performance over control models