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Web-based assessment of operator performance and variability in remote sensing image analysis (WAVARS)

Research project SR/02/121 (Research action SR)


Persons :


Description :

Human perception and interpretation is an indispensable component in many aspects of remote sensing image analysis. Human intervention is a requisite for visual image interpretation, where the interpreter actually performs the analysis. Even in computer-based digital image processing, human screening and interpretation is still needed at certain stages. Next to the remote sensing domain, human intervention plays an important role in other types of geodata processing such as GIS and cartography. Although it is crucial for adequately assessing automated systems’ performance, virtually no research has focussed on operator functioning.

Objective

The goal of the present project is to determine the human factors that influence operator functioning:

- To quantify operator performance in a variety, though limited number, of remote sensing practices using air- and spaceborne remote sensing imagery;
- To characterize operator performance and its determinants (both problem-specific and human factors);
- To identify possible interventions to enhance operator performance and formulate well-founded feedback guidelines regarding the problem definition and the operator efficiency for use in practical settings.

Method

Several experiments testing operator performance will be set up. A try-out will take place in a controlled environment enabling fine-tuning and calibration. In a next step, and in order to obtain sufficiently large data sets, data collection will be performed throughout the worldwide web enabling participants from over the world to take part in the image analysis experiments.

Result

First, the project will lead to a better understanding of human factors in remote sensing image analysis tasks.

A second aim of this research project is the assessment of individual differences that are predictive of high performance on remote sensing image analysis tasks. This should lead to the development of an assessment instrument that is able to identify and select individuals that dispose of the appropriate knowledge, skills, and abilities to perform image analysis tasks with a high level of accuracy for longer periods of time.

Finally, as a third contribution, the current project aims to develop a web-based feedback intervention that may be used for training individuals in image analysis tasks. This feedback intervention would consist of a number of typical remote sensing tasks that individuals can complete online (‘simulation’). After a first trial, individuals will receive constructive feedback and will complete an online feedback facilitation session designed to increase and maintain skill development.


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