Densely Connected Neural Networks for Remote Sensing

The objective of this project is to design a software architecture based on densely-connected neural network to perform automatic targetsegmentation and recognition using training datasets of limited size (low-shot). Deep learning architectures have proved to be extremelyeffective at object detection and recognition, but such capability comes at the cost of having large labeled datasets. Such datasets are notusually available for new threats, which appear continuously in a changing geo-political landscape. The NGA needs to be able to performautomatic target detection and recognition from aerial images when few prior examples of the target are available. The initial Phase I study willfocus on panchromatic Electro-Optical sensors, but the proposed Neural Network architecture is applicable to other types of sensors as well.Transition to other types of sensors is expected in the later stages of the project. The outcome of this project will provide the NGA and the DODwith the much needed capability of being able to detect and react quickly to new threats detectable from a variety of Intelligence, Surveillanceand Reconnaissance payloads.