Title :
Classification of PingPong COSMO-SkyMed imagery using supervised and unsupervised neural network algorithms
Author :
Penalver, M. ; Pratola, C. ; Fabrini, I. ; Frate, F. Del ; Schiavon, G. ; Solimini, D.
Author_Institution :
DISP, Univ. of Rome Tor Vergata, Rome, Italy
Abstract :
The novel instruments of the COSMO-SkyMed (CSK) Earth Observation programme, offer an opportunity to explore at various resolutions the information content of X-band signal backscattered with different polarizations. In spite of their potential to render additional information about an area of interest, speckle noise and artifacts make X-band acquisitions difficult to interpret. This is a motivating scenario to explore what (semi-)automatic procedures might be able to offer. This paper is first attempt to process CSK Stripmap PingPong data using two well-known artificial neural network techniques: the supervised backpropagation multilayer perceptron and the unsupervised self-organizing map.
Keywords :
geophysical image processing; geophysical techniques; image classification; neural nets; self-organising feature maps; COSMO-SkyMed Earth observation programme; CSK Stripmap PingPong data; PingPong COSMO-SKYMED imagery classification; X-band acquisitions; X-band signal; artificial neural network techniques; speckle noise; supervised backpropagation multilayer perceptron; supervised neural network algorithms; unsupervised self-organizing map; Artificial neural networks; Image resolution; Optical imaging; Remote sensing; Synthetic aperture radar; Training; Artificial neural networks; Image classification; Multilayer perceptrons; Self organizing feature maps; Synthetic aperture radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6352269
