Title :
A neural approach to unsupervised classification of very-high resolution polarimetric SAR data
Author :
Burini, A. ; Putignano, C. ; Frate, Del ; Greco, M. Del ; Schiavon, G. ; Solimini, D.
Author_Institution :
GEO-K srl, Rome
Abstract :
Analysis of L-band polarimetric SAR data has not been extensively carried out for undulating, heterogeneous and fragmented landscapes, where classification can become quite challenging. This paper reports results of a study on the pixel-by- pixel unsupervised classification of very-high resolution polarimetric images by self-organizing neural networks.
Keywords :
geophysics computing; neural nets; radar interferometry; self-adjusting systems; terrain mapping; unsupervised learning; L-band polarimetric SAR data analysis; fragmented landscapes; heterogeneous landscapes; high resolution polarimetric SAR data; neural approach; pixel-by- pixel unsupervised classification; polarimetric images; self-organizing neural networks; undulating landscapes; Azimuth; Image resolution; L-band; Land surface; Neural networks; Pixel; Radar scattering; Self organizing feature maps; Space missions; Synthetic aperture radar; SAR; SOM (Self-Organizing Maps); classification;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
DOI :
10.1109/IGARSS.2007.4423767
