Active Selection of Sensor Sites in Remote Sensing Applications

In a data-mining approach, a model for estimation of aerosol optical depth (AOD) from satellite observations is learned using collocated satellite and ground-based observations. For accurate learning of such a spatio-temporal model, it is important to collect ground-based data from a large number of sites. The objective of this project is to determine appropriate locations for the next set of ground-based data collection sites to maximize accuracy of AOD estimation. Ideally, a new site should capture the most significant unseen aerosol patterns and should be the least correlated with the previously observed patterns. We propose achieving this aim by selecting the locations on which the existing prediction model is the most uncertain. Several criteria were considered for site selection, including uncertainty, spatial diversity, similarity in temporal pattern, and their combination. Extensive experiments on globally distributed data over 90 AERONET sites from the years 2005 and 2006 provide strong evidence that sites selected using the proposed algorithms improve the overall AOD prediction accuracy at a faster rate than those selected randomly or based on spatial diversity among sites.