Date of Award


Document Type


Degree Name

Master of Science

Degree Discipline

Electrical Engineering


Recently, computer vision has been promoted by deep learning techniques significantly, where supervised deep learning outperformed other methods such as in image segmentation. However, a large amount of annotated/labeled data is needed for training supervised deep learning models, while such big annotated data is typically unavailable in practice such as in satellite imagery analytics. In order to address this challenge, a novel ensemble unsupervised semantic segmentation method was proposed for image segmentation on satellite images. Specifically, an unsupervised semantic segmentation model was employed to implement foreground- background separation and then be placed within an ensemble model to increase the prediction accuracy further. Experimental results demonstrated that the proposed method outperformed baseline models such as k-means on a satellite image benchmark, the XView2 dataset. The proposed approach provides a promising solution to semantic segmentation in images that will benefit many mission-critical applications such as disaster relief using satellite imagery analytics.

Index Terms - Convolution neural network (CNNs); deep learning; ensemble model; image segmentation; overhead imagery; unsupervised learning

Committee Chair/Advisor

Xiangfang Li

Committee Member

Lijun Qian

Committee Member

Xishuang Dong

Committee Member

Pamela Obiomon


Prairie View A&M University


© 2021 Prairie View A & M University

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

Date of Digitization


Contributing Institution

John B Coleman Library

City of Publication

Prairie View





To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.