PolSAR additive noise estimation based on shadow regions

Additive noise results from the thermal noise of the radar receiver or antenna, and often has an adverse effect on the subsequent applications. To weaken the influence of additive noise, it is crucial to know the actual additive noise level. However, the system noise level is not always provided in some single- or full-polarimetric synthetic aperture radar (PolSAR) products. As one of the typical low backscattering objects (LBOs) in PolSAR images, shadow occurs as soon as the depression angle of the synthetic aperture radar (SAR) system reaches a more value than the slope away from the radar illumination, and thus provides a feasible way to measure additive noise. This paper proposes a framework to automatically extract shadow regions by a trained model using the bag of words (BoW) model with data from different scenes. The additive noise can then be estimated across a large area based on the extracted shadow. The experiments based on single- and full-polarimetric Radarsat-2 data suggest that the shadow-based framework is an effective way to estimate additive noise.