Using a Context-Inclusive Approach to Process Statistical Queries in Raster Data: An Extended Abstract

Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality estimation function. This problem is common in important applications like land-use classification at multiple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the quality estimation function for each candidate model. A recently proposed method of multiscale, multigranular classification has high computational overhead to evaluate the quality estimation functions for various candidate models independently before comparison. In contrast, we propose a context-inclusive approach that controls the computational overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. Experimental evaluation in the application of land-use classification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly while providing comparable classification accuracy.