Inter-polarization Mapping via Gaussian Process Regression for Sentinel-1 EW Denoising

• Published in: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
• Main Authors: Focsa, Adrian, Anghel, Andrei, Datcu, Mihai
• Format: Conference Object
• Language: English
• Published: IEEE - Institute of Electrical and Electronics Engineers 2022
• Subjects: EO Data Science; Arctic
• Online Access: https://elib.dlr.de/193337/; https://elib.dlr.de/193337/1/Inter-polarization_Mapping_via_Gaussian_Process_Regression_for_Sentinel-1_EW_Denoising.pdf; https://ieeexplore.ieee.org/document/9883828

The Sentinel-1 SAR images acquired using the TOPSAR modes i.e., IW and EW on cross-polarization are significantly affected by the thermal noise on low-back-scattering areas. For example, in the arctic and some desert zones both inter- swath and inter-burst noise amplification occurs. In this paper we propose a workflow for removing the thermal noise from Sentinel-1 ground detected SAR images on low back-scattering conditions by employing the co-polarization SAR image and the Gaussian Process Regression. Our processing flow uses the noise vectors provided in the European Space Agency (ESA) ground detected product and scales them such that a slightly over-denoised image is produced. Then, the Gaussian Process Regression is used to map the co-polarization SAR image into the cross-polarization SAR image. Prior to this step, a radiometric correction is applied on the co-polarization data, since its pixel values are heavily dependent on the incidence angle. Finally, the denoised cross-polarization image is obtained as a linear combination between the over-denoised version and the predicted image. Since, the co-polarization channel is employed for the prediction of the missing values in the cross-polarization channel there is no need for co-registration and the de noising procedure is trustworthy.