Talks and Poster Presentations (with Proceedings-Entry):

J. Zhao, M. Chini, P. Matgen, R. Hostache, R. Pelich, W. Wagner:
"An Automatic SAR-Based Change Detection Method for Generating Large-Scale Flood Data Records: The UK as a Test Case";
Talk: IEEE International Geoscience and Remote Sensing Symposium 2019 (IGARSS 2019), Yokohama, Japan; 2019-07-28 - 2019-08-02; in: "2019 IEEE International Geoscience & Remote Sensing Symposium Proceedings", IEEE, CFP19IGA-ART (2019), ISBN: 978-1-5386-9154-0; 6138 - 6141.

English abstract:
The main objective of this study is to introduce and evaluate a SAR-based flood mapping algorithm enabling the automatic generation of a large-scale flood record from the ENVISAT ASAR data archive. The flood mapping algorithm is based on a change detection approach and requires an automatic selection of optimal reference images. The flood mapping algorithm is applied to selected pairs of images to sequentially generate a record of flood extent maps. False alarms caused by water-like areas are reduced using auxiliary data sources such as the Height Above Nearest Drainage (HAND) index derived from topography data. The proposed method is applied to several ENVISAT WS ASAR datasets acquired over the UK and results are validated with a flood extent map derived from aerial photography. Results presented in this paper demonstrate the effectiveness of the methodology.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.