Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric SAR Data

• Published in: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
• Main Authors: Blix, Katalin, Espeseth, Martine, Eltoft, Torbjørn
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2021
• Subjects: VDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555; VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555; Arctic
• Online Access: https://hdl.handle.net/10037/21057; https://doi.org/10.1109/IGARSS39084.2020.9324192

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper addresses the problem of up-scaling full polarimetric (quad-pol) parameters from small quad-pol synthetic aperture radar (SAR) scenes to large dual-pol scenes, using a sophisticated Machine Learning (ML) method, namely the Gaussian Process Regression (GPR). The approach is to let the GPR model learn the relationships between the dual-pol input data and the quad-pol parameters on a quad-pol scene, and then extrapolate the relationships to the whole dual-pol scene. We demonstrate the procedure on two pairs of quadpol Radarsat-2 (RS2) and dual-pol ScanSAR Sentinel-1 (S1) scenes, acquired less than 20 minutes apart. The results are visualised as pixel-wise parametric maps, supported by three quantitative regression performance measures. In addition, we show certainty level maps for the estimated parameters. Our results indicate the potential of using the ML GPR model to upscale quad-pol scenes to large dual-pol images.