Term of Award

Spring 2025

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Mathematical Sciences

Committee Chair

Jiehua Zhu

Committee Member 1

Scott Kersey

Committee Member 2

Yan Wu

Abstract

Synthetic Aperture Radar (SAR) is an active remote sensing system commonly used in aerial reconnaissance. SAR penetrates cloud cover and vegetation by recording reflected energy pulses. The resulting information content is difficult to interpret due to vast clutter data and sparse target data. The time-consuming process of analyzing SAR imagery can be greatly reduced by implementing an Automatic Target Recognition (ATR) algorithm. Convolutional Neural Networks (CNN) are capable of extracting identification features from SAR data content. Further research in this field indicates that high performing models are insufficiently robust due to high clutter correlation among classes in the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Target dataset. This research proposes a robust classification pipeline with the addition of a semantic segmentation model. The result is a classification model that is trained to extract identification features from only target and shadow pixels in the MSTAR Mixed Target dataset.

OCLC Number

1520502677

Research Data and Supplementary Material

No

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