  There is an increasing demand for software that automatically detects and
classifies mobile targets such as airplanes, cars, and ships in satellite
imagery. Applications of such automated target recognition (ATR) software
include economic forecasting, traffic planning, maritime law enforcement, and
disaster response. This paper describes the extension of a convolutional neural
network (CNN) for classification to a sliding window algorithm for detection.
It is evaluated on mobile targets of the xView dataset, on which it achieves
detection and classification accuracies higher than 95%.
