Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data

We consider the problem of detecting objects (such as trees, rooftops, roads, or cars) in remote sensing data including, for example, color or hyperspectral imagery. Many detection algorithms applied to this problem operate by assigning a decision statistic to all, or a subset, of spatial locations in the imagery for classification purposes. In this work we investigate a recently proposed method, called Local Averaging for Improved Predictions (LAIP), which can be used for trading off the classification accuracy of detector decision statistics with their spatial precision. We explore the behaviors of LAIP on controlled synthetic data, as we vary several experimental conditions: (a) the difficulty of the detection problem, (b) the spatial area over which LAIP is applied, and (c) how it behaves when the estimated ROC curve of the detector becomes increasingly inaccurate. These results provide basic insights about the conditions under which LAIP is effective.