Email: hnguyenh [at] uwaterloo [dot] ca

GitHub: https://github.com/howird

LinkedIn: https://www.linkedin.com/in/howird/

Website: https://howird.com

Room: EC4 2038A

Email: chang [dot] liu [at] uwaterloo [dot] ca

LinkedIn: www.linkedin.com/in/hellochang

Website: www.hellochang.com

X/Twitter: twitter.com/hellochangg

May 12, 2023, 11:30 AM, EC4-2101A

Abstract: The Cryosphere-Observing SAR (CryoSAR) system is a Ku- and L-band polarimetric synthetic aperture radar (SAR) system designed to conduct observations of snow and ice on land and over water bodies, and soil moisture status. The CryoSAR system is a fully polarimetric SAR with the capability to conduct single or repeat pass observations for interferometric SAR (InSAR) applications. There is significant interest in the Ku (13.5 GHz) polarimetric response and L-band (1.3 GHz) InSAR measurements of snow on land and water as tools to estimate snow water equivalent, a key variable in water resource management applications and in climate change studies. The CryoSAR radars can be operated independently or together. They can also be deployed on a relatively small aircraft, such as a Cessna 208, which is widely available across North America, Europe and beyond, making the system relatively agile in its deployment. An adjustable mounting system has been designed to enable the instrument to be installed from inside and aircraft and at specified look angles. In fall 2022 and winter 2023, a season-long deployment of the system was conducted in Ontario as part of a Canadian Space Agency-funded project and in support of the Terrestrial Snow Mass Mission. Flights were conducted over selected sites in Ontario including the Haliburton Highlands and Powassan. Field campaigns were also conducted on the ground to provide correlative ground reference data. A combination of traditional field observations of snow properties, and detailed state-of-the-art measurements of microstructure properties were made to quantify the snowpack bulk and stratigraphic characteristics of the snow at the different field sites. This presentation discusses the initial observations made with the CryoSAR system at Ku- and L-band and correlative field measurements. Results focus on the polarimetric responses from snow on land and on lake ice and demonstrate its applicability for terrestrial snow monitoring.

Biography:

Richard Kelly is a Professor in the Department of Geography and Environmental Management at the University of Waterloo. With over 30 years of microwave remote sensing experience, his research focuses on the estimation of seasonal snow mass using microwave observing systems from ground, air and space. He is the PI for JAXA’s AMSR2 operational snow depth product and is the PI for the UW CryoSAR instrument, a CFI-funded imaging radar system with collaborators from across Canada. His current research focuses on supporting the science for the Terrestrial Snow Mass Mission, a dual-frequency Ku-band mission in planning at ECCC and CSA. Prof. Kelly is happy to be a long-term and avid Arsenal FC fan.

LinkedIn: https://ca.linkedin.com/in/amy-tai

One of my primary areas of interest is player action recognition. By using advanced computer vision techniques and machine learning algorithms, I aim to identify and analyze specific player actions on the ice. This could be used in player evaluation techniques as input to analyze their performance and provide coaches and teams with valuable insights into player performance, such as identifying areas for improvement or optimizing their game strategies.

I’m really excited about the work I’ve been doing on goalie pose estimation, too. We’re working on predicting goalie, net, and equipment poses with high accuracy, which will be super valuable for improving goalie action recognition and performance evaluation.

Another area of research I’m interested in, and I’ve worked on during my Master’s is reinforcement learning. I want to improve the generalization ability of RL agents so that they can adapt to new environments and situations more effectively.

Before starting my Ph.D., I worked in the industry doing business analytics for a great company. It was a fantastic experience, and it’s given me a unique perspective on the intersection of academia and industry.

Overall, I’m really passionate about using data and technology to drive our understanding of the world. I’m excited to keep making significant contributions to the field of sports analytics and beyond!

Email: marjan.shahi@uwaterloo.ca

David Clausi
Alexander Wong

Pose Estimation, Action Recognition

Sentinel-1 noise floor removal engine: https://github.com/PeterQLee/sentinel1_denoise_rs