Webinar series: Deep Learning based Super Resolution in Remote Sensing for Agriculture

Remote sensing (RS) satellite images are increasingly used in several monitoring, planning and management applications. Although spectral and spatial resolution of RS satellite images has great importance for the performance of the application, optimal conditions may not be possible in many cases due to physical limitations and the feasibility restrictions. RS Image Super-resolution aims restoring the high-resolution remote sensing images from the given low-resolution images and it demonstrated significant development in parallel to the deep learning algorithms. Recent advances of deep learning based RS image super-resolution methods and their possible impact on agricultural monitoring applications will be evaluated and presented in this webinar by professor Elif Sertel.

Webinar series: Deep Learning based Super Resolution in Remote Sensing for Agriculture

Elif Sertel is a Professor at Geomatics Engineering Department of Istanbul Technical University (ITU). She conducted her PhD dissertation work and had post-doctoral researcher position at Rutgers University, USA as a Fulbright Scholar. Professor Sertel is in the Editorial Board of “Geocarto International” and “International Journal of Digital Earth” journals. She held the Director position at ITU-Center for Satellite Communications and Remote Sensing between 2012 and 2021. Her main research interests are remote sensing, geo-statistics, machine learning based image processing for remote sensing, disaster management, remote sensing for agricultural monitoring and land-cover/use change. She has more than 150 scientific publications in the literature and received multiple prestigious awards in the field.

Live at Zoom and Youtube (14th November 2022):

https://itu-edu-tr.zoom.us/j/97074775930?pwd=b29RV2VGbDNudWhiN1dia0tGLzlEZz09

youtu.be/JWf8l-b4xrk

Moderator: Prof.Dr. B.Berk Ustundag – Vice President / ISAM & Professor of Istanbul Technical University

12th September  9:00 New York/ET

______________14:00 London

______________17:00 İstanbul

______________22:00 Beijing