Honors Theses

Date of Award

Spring 4-30-2020

Document Type

Undergraduate Thesis

Department

Computer and Information Science

First Advisor

Yixin Chen

Second Advisor

Dawn Wilkins

Third Advisor

Feng Wang

Relational Format

PDF

Abstract

Due to the increasing number of available approaches nowadays, choosing the most accurate image semantic segmentation model has become hard. The purpose of this research is to find the best-performing image semantic segmentation model for Cloud classification. For the purpose of this study, a data set of cloud images from the Max Planck Institute for meteorology is used. These images were taken from the by two NASA space satellite.Three main models UNet, PSPNet and FPN were used in combination of 4 differ-ent encoder Inception-ResNet-v2, MobileNet-v2, ResNet-34, and ResNet 101. After training all the models in the Mississippi Center for Super Computing, the results were plotted. Over-all the models turned out broadly similar to each other. Even so, the FPN model with the MobileNet-v2 encoder backbone stood out first followed by the UNet model with the Inception-ResNet-v2 encoder backbone in second place.

Comments

The Evaluation models consisted of three base models

  1. UNet
  2. PSPNet
  3. FPN

Along with 4 Different Encoders

  1. ResNet 34
  2. ResNet 101
  3. MobileNetv2
  4. InceptionResNetv2

Crowd-Sourced DataSet consisting of clouds compiled by Max Planck Institute of Meteorology was used.

Honors_thesis.pdf (3514 kB)
Uploaded the PDF version of dissertation.

Creative Commons License

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

Share

COinS
 
 

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.