A Noise-Aware Deep Learning Model for Sea Ice Classification Based on Sentinel-1 Sar Imagery.

The additive system noise in synthetic aperture radar (SAR) imagery is a challenging problem for the operational use of SAR data for sea ice classification. This noise degrades the performance of the sea ice classification models. The most common way of dealing with this is to remove mean noise profiles from the backscatter intensities as a preprocessing step. In this study we investigate how including the nominal noise profiles as a feature directly into the model affects the classification. Our noise-aware approach can be used in conjunction with any other deep learning model for sea ice classification. Hence our findings pave the way for getting refined and smoother sea ice maps for ice charting. For experimentally evaluating our proposed approach, we train our noise-aware deep model using carefully labeled data consisting of both sea ice data and noise profile. We present validation results considering separate validation data. Our empirical study confirms the superior performance of the CNN model driven by noise-aware characteristics.