Tor Vergata Earth Observation Laboratory

Pulse-Coupled Neural Network for automatic feature extraction
from COSMO-SkyMed and TerraSAR-X imagery

F. Del Frate, G. Licciardi, F. Pacifici, C. Pratola, and D. Solimini



We test an unsupervised neural network approach for extracting features from very-high resolution X-band SAR images. The purpose is thes recognition of building in images of low density urban areas, acquired by COSMO-SkyMed and TerraSAR-X satellites, by means of Pulse Coupled Neural Network (PCNN), a relatively novel unsupervised algorithm based on models of the visual cortex of small mammals. The features retrieved from georeferenced SAR images are compared against the ground truth provided by corresponding optical images. The accuracy yielded by PCNN is quantitatively evaluated and critically discussed, also in comparison with commonly used feature extraction techniques.

Index Terms— Pulse Coupled Neural Network (PCNN), COSMO-SkyMed, TerraSAR-X, unsupervised feature extraction.

2009 IEEE International Geoscience & Remote Sensing Symposium, IGARSS'09, Cape Town, South Africa, 13-17 July, 2009

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