Paper Info
Title
Polarimetric Synthetic-Aperture-Radar Change-Type Classification with a Hyperparameter-Free Open-Set Classifier
Abstract
Synthetic aperture radar (SAR) is a remote sensing technology that can truly operate 24/7. It's an all-weather system that can operate at any time except in the most extreme conditions. Coherent change detection (CCD) in SAR can identify minute changes such as vehicle tracks that occur between images taken at different times. From polarimetric SAR capabilities, researchers have developed decompositions that allow one to automatically classify the scattering type in a single polarimetric SAR (PolSAR) image set. We extend that work to CCD in PolSAR images to identify the type change. Such as change caused by no return regions, trees, or ground. This work could then be used as a preprocessor for algorithms to automatically detect tracks.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00170
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywords
Field
DocType
synthetic-aperture-radar change-type classification,remote sensing technology,all-weather system,extreme conditions,coherent change detection,CCD,vehicle tracks,polarimetric SAR capabilities,scattering type,single polarimetric SAR image set,PolSAR images,type change,hyperparameter-free open-set classifier
Radar tracker,Pattern recognition,Hyperparameter,Synthetic aperture radar,Computer science,Matrix decomposition,Coherence (physics),Preprocessor,Artificial intelligence,Classifier (linguistics),Open set
Conference
ISSN
ISBN
Citations 
2160-7508
978-1-5386-6101-7
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Mark W. Koch19210.60
R. Derek West252.53
Robert Riley300.34
Tu-Thach Quach4356.68