Padhraic Smyth, Usama Fayyad, Michael Burl, Pietro Perona, Pierre Baldi
In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to gener(cid:173) ate 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 signifi(cid:173) cant in terms of quantifying both human and algorithm detection performance.