Co-sponsored by: Tiago H. Falk

In order to overcome technical challenges, reduce the risk, shorten the development period and lower the cost of space missions, government scientists and engineers at the Canadian Space Agency (CSA) have developed cutting-edge innovative technologies. To tackle the huge data volume generated by satellites and the bottleneck to transmit the data to ground in the downlink channel Dr. Qian has invented multiple onboard near-lossless data compression techniques that have been patent granted. Modern satellites produce scientific data with large dimensionality, which presents a challenge to traditional satellite data processing techniques. Many conventional data processing methods may not be applied without dimensionality reduction as a pre-processing step. He developed novel nonlinear dimensionality-reduction techniques using locally linear embedding (LLE, which is used in arranging human facial images and words in semantic space) as well as techniques to simultaneously reduce noise and dimensionality of multi-dimensional satellite data. To fill void in the assessment of satellite image quality after post-processing or enhancement Dr. Qian proposed four reduced-reference quality metrics in the absence of a full reference.These reduced-reference metrics have been well used to assess the satellite image quality.In this seminar, Dr. Qian will briefly introduce a number of innovative satellite signal processing technologies developed at CSA for helping overcome technical challenges in the development of Canadian space technology and missions. He will also show the experimental results of the technologies and demonstrate how the innovative technologies benefit scientific community and better serve the Earth observation applications.

Speaker(s): Dr. Shen-En Quan,

Location:
Room: Suite 6900
INRS-EMT, Universite du Quebec
800, Rue de la Gauchetière Ouest
Montreal, Quebec