Inferring Ground Truth from Subjective Labelling of Venus Images
Abstract
In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite significant in terms of quantifying both human and algorithm detection performance.
Additional Information
© 1995 Massachusetts Institute of Technology. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration and was supported in part by ARPA under grant number N00014-92-J-1860.Attached Files
Published - 949-inferring-ground-truth-from-subjective-labelling-of-venus-images.pdf
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Additional details
- Eprint ID
- 55562
- Resolver ID
- CaltechAUTHORS:20150305-153627706
- NASA
- N00014-92-J-1860
- Office of Naval Research (ONR)
- Advanced Research Projects Agency (ARPA)
- Created
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2015-03-06Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field