Integrating neural networks with image pyramids to learn target context

AImtraet--The utility o f 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 o f its sensitivity, than a network which utilizes more complex object-taned features. Next, an architecture that supports coarse-to-fine search, context learning and data fusion is tested. The accuracy o f 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 o f detection. Such neural network/pyramid target detectors should be useful components in both user assisted search and fully automatic target recognition and monitoring systems.