Title :
Unsupervised change detection frameworks for very high spatial resolution images
Author :
Pacifici, F. ; Padwick, C. ; Marchisio, G.
Abstract :
Two different unsupervised change detection techniques are here investigated. The first method is based on pulse-coupled neural networks, which show invariance to object scale, shift or rotation. The second method, based on the normalized cross-correlation, is suited to work in an “on-line” processing as more images are made available, for example in case of natural events such as an earthquake or tsunami. The performances of the algorithms have been evaluated on pairs of QuickBird, WorldView-1 and WorldView-2 images taken over Atlanta (U. S. A.), Washington D. C. (U. S. A.), and Conception (Chile).
Keywords :
correlation methods; image recognition; neural nets; unsupervised learning; QuickBird image; WorldView-1 image; WorldView-2 image; normalized cross correlation; on-line processing; pulse coupled neural networks; unsupervised change detection framework; very high spatial resolution image; Artificial neural networks; Buildings; Correlation; Earthquakes; Neurons; Pixel; Spatial resolution; Normalized cross-correlation; nsupervised change detection; pulsecouple neural networks;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4244-9565-8
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2010.5650560
