DocumentCode
143815
Title
Analysis of polarimetric features from CTLR compact polarimetric SAR data for discriminating oil slick damping status
Author
Yu Li ; Hui Lin ; Yuanzhi Zhang ; Jie Chen
Author_Institution
Inst. of Space & Earth Inf. Sci., Chinese Univ. of Hong Kong, Hong Kong, China
fYear
2014
fDate
13-18 July 2014
Firstpage
3490
Lastpage
3493
Abstract
Polarimetric features retrieved from CTLR (circularly transmit and linearly receive) Synthetic Aperture Radar (SAR) data was analysed in details. A new parameter, namely, Damping Status Sensitivity Index (DSSI) was proposed for quantitatively evaluating the PolSAR characteristics´ capability of discriminating different damping patterns of oil slicks and clean seawater. The L-band Uninhabited Aerial Vehicle SAR (UAVSAR) data was utilized in the experiments. It was shown that polarimetric characteristics retrieved from CTLR compact polarimetric SAR data were nearly as same as those derived from fully polarimetric SAR data and can be applied for discriminating different damping status of oil spill and look-likes.
Keywords
marine pollution; oil pollution; radar polarimetry; seawater; synthetic aperture radar; water quality; CTLR compact polarimetric SAR data; DSSI; L-band uninhabited aerial vehicle SAR data; PolSAR characteristic capability; Synthetic Aperture Radar data; UAVSAR data; circularly transmit and linearly receive; clean seawater; damping status sensitivity index; fully polarimetric SAR data; oil slick damping pattern discrimination; oil slick damping status discrimination; oil spill damping status; polarimetric characteristic; polarimetric feature analysis; Damping; Decision support systems; Indexes; Sea surface; Spaceborne radar; Synthetic aperture radar; Classification; Compact Polarimetry; Oil Spill; SAR; Weak damping;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location
Quebec City, QC
Type
conf
DOI
10.1109/IGARSS.2014.6947234
Filename
6947234
Link To Document