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Classifying Maritime Vessels from Satellite Imagery with HyperNEAT

Maritime data uniquely challenges imagery analysis. Such data suffers from degradation, limited samples, and varied formats. To this end, the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach is investigated in addressing such challenges for classifying maritime vessels in a satellite imagery data set. The results show that HyperNEAT learns to extract features that allows better classification than those from Principal Component Analysis (PCA) and robust to differences in presentation of data. Furthermore, HyperNEAT enables a unique capability to scale trained solutions to different image resolutions.