  This paper explores the similarities of output layers in Neural Networks
(NNs) with logistic regression to explain importance of inputs by Z-scores. The
network analyzed, a network for fusion of Synthetic Aperture Radar (SAR) and
Microwave Radiometry (MWR) data, is applied to prediction of arctic sea ice.
With the analysis the importance of MWR relative to SAR is found to favor MWR
components. Further, as the model represents image features at different
scales, the relative importance of these are as well analyzed. The suggested
methodology offers a simple and easy framework for analyzing output layer
components and can reduce the number of components for further analysis with
e.g. common NN visualization methods.
