Deep Learning for Recognizing Mobile Targets in Satellite Imagery
This work addresses the need for automated target recognition in satellite imagery for applications such as economic forecasting and disaster response, but it is incremental as it builds on existing CNN methods.
The paper tackled the problem of automatically detecting and classifying mobile targets like airplanes, cars, and ships in satellite imagery by extending a convolutional neural network to a sliding window algorithm, achieving detection and classification accuracies over 95% on the xView dataset.
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%.