Unsupervised change detection frameworks for very high spatial resolution images

Author

Pacifici, F. ; Padwick, C. ; Marchisio, G.

fYear

2010

fDate

25-30 July 2010

Firstpage

2567

Lastpage

2570

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Conference_Location

Honolulu, HI

ISSN

2153-6996

Print_ISBN

978-1-4244-9565-8

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2010.5650560

Filename

5650560