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Here we demonstrate how Deep Neural Network (DNN) detections of multipleconstitutive or component objects that are part of a larger, more complex, andencompassing feature can be spatially fused to improve the search, detection,and retrieval (ranking) of the larger complex feature. First, scores computedfrom a spatial clustering algorithm are normalized to a reference space so thatthey are independent of image resolution and DNN input chip size. Then,multi-scale DNN detections from various component objects are fused to improvethe detection and retrieval of DNN detections of a larger complex feature. Wedemonstrate the utility of this approach for broad area search and detection ofSurface-to-Air Missile (SAM) sites that have a very low occurrence rate (only16 sites) over a ~90,000 km^2 study area in SE China. The results demonstratethat spatial fusion of multi-scale component-object DNN detections can reducethe detection error rate of SAM Sites by $>$85% while still maintaining a 100%recall. The novel spatial fusion approach demonstrated here can be easilyextended to a wide variety of other challenging object search and detectionproblems in large-scale remote sensing image datasets.

Broad Area Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks