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