Anseel, Frederik

De Wulf, Robert

Duyck, Wouter

Gardin, Soetkin

Van Laere, Sébastien

Vancoillie, Frieke

English

Ghent University Academic Bibliography

The results of the control group already showed significant variability amongst operators that could partly be explained by human factors. In order to get valuable results, it is extremely important to reach a large group of experts, so interested researchers are invited to participate in the experiment.  Two participants will win a Dell Latitude Netbook!Operator Variability in Remote Sensing Image AnalysisSoetkin Gardin1,  Sébastien Van Laere3, Frieke Van Coillie1, Wouter Duyck3, Frederik Anseel2 and Robert De Wulf1 1 Laboratory of Forest Management and Spatial Information Techniques, UGent2 Department of Personnel Management, Work and Organizational Psychology, UGent3 Department of Experimental Psychology, UGentContact: Soetkin.Gardin@UGent.beWithin the framework of climate change and forest biodiversity loss, the importance of forest management has gained more and more attention worldwide. The conductance of a forest inventory is a very labor‐intensive job, therefore field work is often replaced by remote sensing image analysis and interpretation. Although this image processing is highly automated, there is still a considerable human intervention, e.g. During georeferencing, operating  the computers, building the algorithms, selecting training data etc. The human operator is present in every step of the way, making decisions and choices. The fact that another operator would probably make different choices is often overlooked.This research aims at:1. Quantifying operator variability2. Identifying human and problem‐specific factors that influence variability3. Identifying methods to enhance operator performanceData collection will be performed over the world wide web, a web application has been developed which contains personality and image analysis tasks.Overview Factors•Demographics                                (Age, Gender,...)•Cognitive Variables                 (Visual Working Memory Span,...)•Non‐Cognitive Variables  (Personality, Motivation,...)Human factors• Image Quality                             (Spatial Resolution, Colour,...)•Ancillary Information    (Precognition, Information Type,...)Problem specific factorsWeb ApplicationConclusionsForestsat 2010, Lugo Research funded by the Belgian Science PolicyCallSome first results were obtained from a control group  (n=120) of students and personnel of the Ghent University.There were six different digitizing tasks implemented in the web application. For each task, two accuracy measures were calculated and are shown in table 1. The first measure (thematic accuracy) represents the proportion between hit rate and false alarms/misses. The second measure (positional accuracy) represents the mean distance between the digitized and reference elements.First ResultsFitted : age + sex + speed + experience + ex + emo + con + agree + open + mot + com +performance0.65 0.70 0.75 0.80 0.850.60.70.8variable b SE(b) t p R² Δ R²step 1 Sex 0.04 0.01 4.07 0.00 0.13 0.13Age 0.00 0.00 0.95 0.35step 2 Speed ‐3.14 0.79 ‐3.99 0.00 0.28 0.15Experience 0.01 0.01 2.00 0.05step 3 Extraversion ‐0.01 0.01 ‐1.65 0.10 0.32 0.04Emotional stability 0.01 0.01 1.19 0.24Conscientiousness ‐0.01 0.01 ‐1.22 0.23Agreeableness ‐0.01 0.01 ‐1.49 0.14Openness 0.00 0.01 ‐0.32 0.75step 4 Motivation ‐0.01 0.01 ‐1.44 0.15 0.39 0.07Comparative anxiety ‐0.04 0.01 ‐3.56 0.00step 5Conscientiousness : Speed 3.03 1.22 2.48 0.01 0.43 0.03Factor  M  SD  Min Max SEThematic Accuracy1 Lamp posts 0.76 0.06 0.54 0.88 0.0062 Trees 0.92 0.08 0.54 0.98 0.0073 Water 0.81 0.04 0.64 0.91 0.0044 Parcels 0.88 0.06 0.36 0.94 0.0065 Grape vines 0.80 0.12 0.42 1.00 0.0126 Roads 0.71 0.10 0.41 0.90 0.009Positional Accuracy7 Grape vines 1.92 0.29 1.26 2.68 0.0278 Lamp posts 6.29 0.91 4.45 9.75 0.0839 Trees 4.63 0.50 3.71 6.24 0.04610 Water 2.49 0.29 1.76 4.23 0.02811 Parcels 2.62 0.24 2.18 4.03 0.02212 Roads 2.64 0.18 2.20 3.08 0.016Mean (M), Standard Deviation (SD), Minimum (Min), Maximum (Max) and Standard Error (SE) of the different performance parameters (N=120)Linear regression of performance with human factors(a) Steps                                                                             (b) Predicted Performance versus Observed Performance

Assessing human factors in remote sensing image analysis: a web-based approach

https://biblio.ugent.be/publication/1220689/file/1220690

Assessing human factors in remote sensing image analysis: a web-based approach

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