Journal
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
Volume -, Issue -, Pages 10019-10022Publisher
IEEE
DOI: 10.1109/igarss.2019.8900150
Keywords
Polarimetric synthetic aperture radar (PolSAR) images; physical scattering pattern; complex-valued convolutional neural network (CNN); H-A-alpha target decomposition; F-SAR
Categories
Funding
- China Scholarship Council (CSC)
Ask authors/readers for more resources
Full-polarimetric synthetic aperture radar (SAR) images have the ability to provide physical patterns of the earth observation, no more than geometric information. In order to learn physical patterns from non-full-polarimetric SAR images, a complex-valued CNN is leveraged to learn a model containing physical parameters. The parameters are learned from the original complex scattering matrix of full-polarimetric SAR images and they can be adopted to extract physical patterns from non-full-polarimetric SAR images. Cloude and Pottier's H-alpha division, as the annotation principle, is computed by way of coherence matrix. We perform experiments on (German Aerospace Center) DLR's full-polarimetric, airborne F-SAR data, demonstrating that extracting physical patterns from non-full-polarimetric images is feasible. The comparative results illustrate that: 1) The best physical categoric patterns can be extracted from HV and VH polarimetric images in general, while performance from HH and VV polarimetric images are limited; 2) Cross-polarimetric SAR images have greater ability for surface and volume scattering, while co-polarimetric ones are better for multiple scattering extraction.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available