Machine Learning simulations of quad-polarimetric features from dual-polarimetric measurements over sea ice

• Main Authors: Blix, Katalin, Espeseth, Martine, Eltoft, Torbjørn
• Format: Article in Journal/Newspaper
• Language: English
• Published: VDE VERLAG GMBH 2018
• Subjects: VDP::Technology: 500::Environmental engineering: 610; VDP::Teknologi: 500::Miljøteknologi: 610; VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452; VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452; Sea ice; Gaussian Process Regression; Machine Learning; Arctic
• Online Access: https://hdl.handle.net/10037/14787

Source at https://www.vde-verlag.de/proceedings-en/454636136.html . In this paper, we investigated the capabilities of the Gaussian Process Regression (GPR) algorithm in predicting of two quad-polarimetric parameters (relevant for sea ice analysis) from 6-dimensional dual-polarimetric input vectors. The GRP is trained on few hundred samples selected randomly from an image subset, and tested on the entire image. The performance is assessed by visual comparisons, and by quantifying two regression performance statistical measures. The results of the regression showed big variations from scene to scene, and between the estimated output parameters, but the overall assessment is that the method gave surprisingly good correspondence to the real quad-polarimetric parameters.