Integrating neural networks with image pyramids to learn target context

The utility of combining neural networks with pyramid representations for target detection in aerial imagery is explored. First, it is shown that a neural network constructed using relatively simple pyramid features is a more effective detector, in terms of its sensitivity, than a network which utilizes more complex object-tuned features. Next, an architecture that supports coarse-to-fine search, context learning and data fusion is tested. The accuracy of this architecture is comparable to a more computationally expensive non-hierarchical neural network architecture, and is more accurate than a comparable conventional approach using a Fisher discriminant. Contextual relationships derived both from low-resolution imagery and supplemental data can be learned and used to improve the accuracy of detection. Such neural network/pyramid target detectors should be useful components in both user assisted search and fully automatic target recognition and monitoring systems