Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery
In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or 'confidence') to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection theory underpinning this general approach assumes that a yes/no decision must be made, individually, for each location. In some applications, however, the precise location of the detected objects may be less important than knowing how many total objects there are. In this work we propose two methods that can permit a generic detection algorithm to gradually lower the spatial certainty, or resolution, of its detections in order to improve the accuracy of the overall number of detected objects. We validate the proposed methods on a controlled synthetic dataset as well as a real dataset from previously published work on solar photovoltaic array detection in color aerial imagery.
