@inproceedings {815, title = {AUTOMATIC FEATURE LEARNING OF SAR IMAGES FOR SEA ICE CONCENTRATION ESTIMATION USING FEED-FORWARD NEURAL NETWORKS}, booktitle = {IGARSS}, year = {2014}, address = {Quebec City}, abstract = {

A\ two\ layer\ feed\ forward\ neural\ network\ is\ used\ to\ estimate\ ice\ concentration from SAR images directly in this research. SAR image patches are used as input. The CIS (Canadian Ice Service) ice concentration image analyses are used to train the neural network. The experiment shows that the simple neural network can be used to generate a reasonable ice concentra- tion with no preprocessing to the SAR images.

}, keywords = {neural network, regression, SAR, Sea ice concentration}, author = {L Wang and A K. Scott and D A. Clausi} } @article {14, title = {Ice concentration estimation from dual-polarized SAR images using deep convolutional neural networks}, journal = {IEEE Transactions on Geoscience and Remote Sensing }, year = {2014}, abstract = {

High resolution ice concentration maps are of great interest for shipping and operations in the Arctic. A convolutional neural network (CNN) has been used to estimate ice concentration from C-band dual-polarized RADARSAT-2 satellite images in the melt season. SAR images are used as input and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation-based post processing, the errors of the generated ice concentration maps compared to image analyses are less than 10\% on average. The CNN is demonstrated to produce ice concentration maps with more details than image analyses. Reasonable ice concentration estimations are made in the melt season, and regions of low ice concentration ice are also well
captured.

}, keywords = {convolutional neural network, Sea ice concentration, synthetic aperture radar}, author = {L Wang and A K. Scott and L Xu and D A. Clausi} }