Estimating Noise Floor in Sentinel-1 Images With Linear Programming and Least Squares

Abstract

Sentinel-1 is a synthetic aperture radar platform that provides free and open-source images of the Earth. A product type of Sentinel-1 is ground range detected (GRD), which records intensity while discarding phase information from the radar backscatter. Especially in cross-polarized GRD images, there are noticeable intensity changes throughout the image that are caused by amplifying the noise floor of the signal, which varies due to the nonuniform radiation pattern of the satellite's antenna. While Sentinel-1 has instrument processing facility (IPF) software to estimate the noise floor, even in the newer versions (3.1 or above) of the IPF software there are still instances where the estimates provided do not fit the actual noise floor in the image, which is particularly noticeable in transitions between adjacent subswaths. In this work, we propose a method that reduces the impact of the varying noise-floor throughout the image. The method models the intensity of the noise floor to be a power function of the radiation pattern power. The method divides the swath into several sections depending on the location of the local minimum and maximum of the radiation pattern power with respect to the range. The parameter estimation is portrayed as a geometric programming problem that is transformed into a linear programming problem by logarithmic transformation. Affine offsets are computed for each subswath by a weighted least squares approach. Vast improvement is found on extra-wide (EW) and interferometric wide (IW) Sentinel-1 modes over cross-polarized images. Code implementation is available at https://github.com/PeterQLee/sentinel1_denoise_rs.