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http://dx.doi.org/10.7780/kjrs.2022.38.5.2.7

Exploitation of Dual-polarimetric Index of Sentinel-1 SAR Data in Vessel Detection Utilizing Machine Learning  

Song, Juyoung (School of Earth and Environmental Sciences, Seoul National University)
Kim, Duk-jin (School of Earth and Environmental Sciences, Seoul National University)
Kim, Junwoo (Future Innovation Institute, Seoul National University)
Li, Chenglei (School of Earth and Environmental Sciences, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.5_2, 2022 , pp. 737-746 More about this Journal
Abstract
Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p1, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ0, VV, γ0, VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.
Keywords
Vessel detection; Eigen-decomposition; Sentinel-1; SAR polarimetry; AIS;
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