Title :
Pulse Coupled Neural Network for automatic features extraction from COSMO-Skymed and TerraSAR-X imagery
Author :
Del Frate, Fabio ; Licciardi, Giorgio ; Pacifici, Fabio ; Pratola, Chiara ; Solimini, Domenico
Author_Institution :
Dipt. di Inf., Sist. e Produzione, Tor Vergata Univ., Rome, Italy
Abstract :
In this paper we test an unsupervised neural network approach for extracting features from very high resolution X-band SAR images. The purpose of this study is buildings recognition 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 geo-referenced 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.
Keywords :
feature extraction; geophysical image processing; neural nets; remote sensing by radar; synthetic aperture radar; COSMO-Skymed imagery; Pulse Coupled Neural Network; TerraSAR-X imagery; automatic features extraction; visual cortex; Brain modeling; Feature extraction; Image recognition; Image resolution; Image retrieval; Neural networks; Optical pulses; Satellites; Testing; Urban areas; COSMO-Skymed; Pulse Coupled Neural Network (PCNN); TerraSAR-X; features extraction;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5417783