Wide area Motion Monitoring System (WAMS)

Noise interference is often a major challenge in signal recognition. In this project, our goal is to monitor ground and infrastructure movement based on data acquired using smart sensor technology. While related works have been extensively discussed in the literature, our contribution lies in exploring a reverse engineering procedure to remove noise from raw captured data. We first detect the noise in images, which provide visual cues and make recognition comparatively easier than recognizing irregularities in signals. Novel image/signal quality assessment (IQA/SQA) metrics and de-noise algorithms will be introduced. We will also investigate the effectiveness of image/signal fusion with cross validation. Our objective is to automate the computational steps and improve the time and quality performance. This project will help 3vG in advancing the existing limits of InSAR technology and removing barriers to its operational use, leading to economic benefits in Canada.

Faculty Supervisor:

Irene Cheng;Amirali Baniasadi

Student:

Partner:

3vGeomatics

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta; University of Victoria

Program: