Abstract

The goal of this thesis is the development of LambdaNet, a new type of network architecture for the performance of unstructured change detection. LambdaNet combines concepts from Siamese and semantic segmentation architectures, and is capable of identifying and localizing the significant differences between image pairs while simultaneously disregarding background noise. Changes are marked at the pixel level, by interpreting change detection as a binary (change/no change) classification problem.

Development of this architecture began with an evaluation of several candidate models, inspired by other successful network architectures and layers, including VGG, ResNet, and the Res2Net layer. Once the best performing LambdaNet architecture was determined, it was extended to incorporate a multi-class version of change detection. Referred to as directional change, this technique allows segmentation-based output of change information in four different classes: No change, additive change, subtractive change, and exchange.

Lastly, change detection is not the only unstructured operation of interest. One of the most successful unstructured techniques is that of artistic style transfer. This method allows information from a style image to be merged into a supplied content image. In order to implement this technique, a new variant of LambdaNet was developed, called LambdaStyler. This network is capable of learning multiple artistic styles, which can then be selected for application to the desired content image.

Library of Congress Subject Headings

Computer network architectures--Design; Image analysis

Publication Date

12-2019

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)

Advisor

Andreas Savakis

Advisor/Committee Member

Raymond Ptucha

Advisor/Committee Member

Cory Merkel

Campus

RIT – Main Campus

Plan Codes

CMPE-MS

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