Home // MMEDIA 2017, The Ninth International Conferences on Advances in Multimedia // View article
Authors:
Paolo Addesso
Maurizio Longo
Rocco Restaino
Gemine Vivone
Keywords: Thermal Sharpening; Cloud Masking; Multitemporal Analysis; Bayesian Smoothing; Robustness
Abstract:
Many remote sensing applications require the availability of radiometric surface temperature information with both high acquisition rate and high spatial resolution, but unfortunately this requirement is still not achievable through a single sensor. However, the huge amount of remote sensed data provided by several heterogeneous spaceborne sensors allows to use data fusion in order to overcome this issue. In this paper, we propose a method for sharpening thermal images in a nearly real-time scenario, also capable to deal with missing data due to cloudy pixels. Moreover, we analyze the robustness of the method with respect to cloud mask misclassifications and assess its effectiveness via numerical simulations based on SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data.
Pages: 89 to 94
Copyright: Copyright (c) IARIA, 2017
Publication date: April 23, 2017
Published in: conference
ISSN: 2308-4448
ISBN: 978-1-61208-548-7
Location: Venice, Italy
Dates: from April 23, 2017 to April 27, 2017