Yong U. Sinn

Date of Award


Document Type


Degree Name

Master of Science in Computer Engineering


Department of Electrical and Computer Engineering

First Advisor

Kenneth M. Hopkinson, PhD


This research presents and solves constrained real-world problems of using synthetic data to train artificial neural networks (ANNs) to detect unresolved moving objects in wide field of view (WFOV) electro-optical/infrared (EO/IR) satellite motion imagery. Objectives include demonstrating the use of the Air Force Institute of Technology (AFIT) Sensor and Scene Emulation Tool (ASSET) as an effective tool for generating EO/IR motion imagery representative of real WFOV sensors and describing the ANN architectures, training, and testing results obtained. Deep learning using a 3-D convolutional neural network (3D ConvNet), long short term memory (LSTM) network, and U-Net are used to solve the problem of EO/IR unresolved object detection. U-Net is shown to be a promising ANN architecture for performing EO/IR unresolved object detection. In two of the experiments, U-Net achieved 90% and 88% pixel prediction accuracy. In addition, the results show ASSET is capable of generating sufficient information needed to train deep learning models.

AFIT Designator


DTIC Accession Number