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 groundbased observations. For accurate learning of such a spatiotemporal 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 pr...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Debasish Das, Zoran Obradovic, Slobodan Vucetic
• Keywords: Aerosol Optical Depth, AOD Estimation, Data Mining, Ground-based Data, ICDM 2009