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 × 62 km (for AMSR2, 6.9 GHz) compared with the 93 m × 87 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 R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 R2 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.
Highlights
D ATA fusion of satellite sensors has a growing potential to improve applications in Earth observation (EO) with the growing number of EO satellites being launched
Ice charts are produced for areas where there is maritime traffic, and since the majority of the ships in Greenland waters try to avoid sailing in sea ice, the ice maps are focused on showing areas of open water in between sea ice
In the case of very different spatial resolution as with Sentinel-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 (AMSR2) microwave radiometry, stacking the sources leads to an enormous amount of unnecessary redundant convolutions performed on many channels, as data must be resampled to the highest resolution before they are fed to the network
Summary
D ATA fusion of satellite sensors has a growing potential to improve applications in Earth observation (EO) with the growing number of EO satellites being launched. While the proposed network architecture is generally useful when sensors have large resolution differences, it is applied here to the problem of generating Arctic sea ice maps from synthetic aperture radar (SAR) images combined with microwave radiometer (MWR) data. The contributions of this article are: 1) a general CNN architecture for combining advantages of data acquired using two or more sensors of very different ground resolution; 2) the application of the methodology to automatic generation of sea ice maps; and 3) improvements in CNN training by using “soft” probabilities in applications where these are available, such as the sea ice concentrations (SICs) used here. In terms of the CNN’s architecture, the proposed method provides several new concepts It concatenates very low-resolution sensor data at a deeper layer in the CNN model in order to save convolutions, i.e., computations, rather than stacking all data as an extended band image.
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