Author

Terence J. Yi

Date of Award

3-2020

Document Type

Thesis

Degree Name

Master of Science in Computer Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Robert C. Leishman, PhD

Abstract

In situations where global positioning systems are unavailable, alternative methods of localization must be implemented. A potential step to achieving this is semantic segmentation, or the ability for a model to output class labels by pixel. This research aims to utilize datasets of varying spatial resolutions and locations to train a fully convolutional neural network architecture called the U-Net to perform segmentations of aerial images. Variations of the U-Net architecture are implemented and compared to other existing models in order to determine the best in detecting buildings and roads. A final dataset will also be created combining two datasets to determine the ability of the U-Net to segment classes regardless of location. The final segmentation results will demonstrate the overall efficacy of semantic segmentation for different datasets for potential localization applications.

AFIT Designator

AFIT-ENG-MS-20-M-075

Share

COinS