UNSUPERVISED CHANGE DETECTION FRAMEWORKS FOR VERY HIGH SPATIAL RESOLUTION IMAGES

被引：2

作者：

Pacifici, F. ^{[1
]}

Padwick, C. ^{[1
]}

Marchisio, G. ^{[1
]}

机构：

[1] DigitalGlobe Inc, Longmont, CO 80503 USA

来源：

2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年

关键词：

Normalized cross-correlation; pulse-couple neural networks; nsupervised change detection;

D O I：

10.1109/IGARSS.2010.5650560

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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).

引用

页码：2567 / 2570

页数：4

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