부산대학교 도서관

The detection of sea ice leads is a prerequisite for the estimation of ice freeboard and thickness from altimeter data. The classification of altimeter waveforms is generally performed using statistical parameters on the echo power or machine learning approaches directly on the waveforms. The validation and optimization of such algorithms can be carried out using a set of reference cases provided by Earth Observation images. In this paper, we first developed a new lead detector based on Sentinel-1 (S-1) synthetic aperture radar (SAR) images. A robust and consistent methodology for the joint assessment of Altimeter and SAR leads detector is then provided. We propose to fully account for the 2-D geometric problem when comparing the 1-D altimeter track and 2-D SAR image. The surface of the lead intersecting the altimeter footprint and its distance to nadir are considered here. Based on collocated Sentinel-3 (S-3) altimeter data and S-1 images, the performance of our S-3 lead detector is fully assessed. A new parameterization is found resulting in a better tradeoff between good detection and false alarm rate. A similar analysis is performed using AltiKa altimeter data, showing enhanced performance for S-3 altimeter data acquired in Delay-Doppler mode with reduced off-nadir returns. [ABSTRACT FROM AUTHOR]