Neural network based automatic sea ice classification for CL-pol RISAT-1 imagery
Ressel R, Singha S, Lehner S (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2016-November
Pages Range: 4835-4838
Conference Proceedings Title: International Geoscience and Remote Sensing Symposium (IGARSS)
Event location: Beijing, CHN
ISBN: 9781509033324
DOI: 10.1109/IGARSS.2016.7730261
Abstract
SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, dual polarimetric, or even better fully polarimetric data offer a higher information content which allows for a more reliable automated sea ice analysis. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, hybrid polarimetric acquisitions offer a trade-off between the mentioned objectives. With the advent of the RISAT-1 satellite platform, we are able to explore the potential of hybrid dual pol acquisitions for sea ice analysis and classification. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. We present first results on a dataset acquired off the eastern Greenland coast.
Involved external institutions
Germany (DE)How to cite
APA:
Ressel, R., Singha, S., & Lehner, S. (2016). Neural network based automatic sea ice classification for CL-pol RISAT-1 imagery. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4835-4838). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Ressel, Rudolf, Suman Singha, and Susanne Lehner. "Neural network based automatic sea ice classification for CL-pol RISAT-1 imagery." Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2016. 4835-4838.
BibTeX: Download