Assessment of Synthetic Aperture Radar Image Preprocessing Methods for Iceberg and Ship Recognition with Convolutional Neural Networks

The classification of objects and man-made structures on the ocean surface using synthetic aperture radar (SAR) imagery finds an important use in monitoring for icebergs and sea ice. Convolutional Neural Network (CNN) method can be employed to effectively classify objects imaged through SAR data. In this paper, the CNN performance is evaluated when three different preprocessing procedures are applied to the SAR image data to prepare the CNN's input data. Segmentation or normalization algorithms are implemented in the three procedures. Experimental results demonstrate an improvement over unsegmented and un-normalized images. Performance metrics for all three methods are approximately 94%, indicating that while some image preprocessing is required to achieve higher performance, the CNN tested is robust to the noise present in the SAR images used.