A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion

Abstract

With a growing number of different satellite sensors, data fusion offers great potential in many applications. In this work, a convolutional neural network (CNN) architecture is presented for fusing Sentinel-1 synthetic aperture radar (SAR) imagery and the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The CNN is applied to the prediction of Arctic sea ice for marine navigation and as input to sea ice forecast models. This generic model is specifically well suited for fusing data sources where the ground resolutions of the sensors differ with orders of magnitude, here 35 km <inline-formula> <tex-math notation= LaTeX >$\\times62$ </tex-math></inline-formula> km (for AMSR2, 6.9 GHz) compared with the 93 m <inline-formula> <tex-math notation= LaTeX >$\\times87$ </tex-math></inline-formula> m (for sentinel-1 IW mode). In this work, two optimization approaches are compared using the categorical cross-entropy error function in the specific application of CNN training on sea ice charts. In the first approach, concentrations are thresholded to be encoded in a standard binary fashion, and in the second approach, concentrations are used as the target probability directly. The second method leads to a significant improvement in <inline-formula> <tex-math notation= LaTeX >$R^{2}$ </tex-math></inline-formula> measured on the prediction of ice concentrations evaluated over the test set. The performance improves both in terms of robustness to noise and alignment with mean concentrations from ice analysts in the validation data, and an <inline-formula> <tex-math notation= LaTeX >$R^{2}$ </tex-math></inline-formula> value of 0.89 is achieved over the independent test set. It can be concluded that CNNs are suitable for multisensor fusion even with sensors that differ in resolutions by large factors, such as in the case of Sentinel-1 SAR and AMSR2.