College of LAS Events

Back to Listing

If you will need disability-related accommodations in order to participate, please email the contact person for the event.
Early requests are strongly encouraged to allow sufficient time to meet your access needs.

Statistics Seminar - Zhengyuan Zhu (Iowa State U)

Event Type
Xiaofeng Shao
Engineering Hall 106B8
Sep 5, 2019   3:30 pm  
Originating Calendar
Department of Statistics Event Calendar

Remote sensing data from satellite are used in a variety of disciplines. In many applications a seamless dataset is needed. However, most satellite data have large amount of missing data due to a number of factors such as cloud cover, other abnormal atmospheric conditions, and sensor specific problem. In this talk we introduce a general spatiotemporal satellite image imputation method based on sparse functional data analysis techniques. The latent spatiotemporal process is imputed from observations consisting of a few longitudinally repeated satellite images, which are themselves contaminated with noise and partially observed due to cloud coverage and other reasons. Under this new observation model we provide theoretical justifications for the proposed imputation approach. Practical analyses on Landsat data were conducted to illustrate and validate our algorithm. A comparison with existing gap-filling methods shows that our proposed algorithm significantly outperforms the other methods in terms of having smaller prediction errors. The proposed algorithm is implemented in R and Rcpp and is available as an R package STFIT.

link for robots only