Paper Info
Title
An a-contrario approach for unsupervised change detection in radar images
Abstract
This paper presents a new approach for unsupervised change detection in pairs of Synthetic Aperture Radar (SAR) images. As changes to detect can have various sizes and intensities which are a priori unknown in most applications, we propose a multiscale approach without considering any a priori information. Using multiscale series of a cumulant-based Kullback-Leibler divergence (CKLD) measure computed between two dates, changes are characterized as areas where the CKLD values vary a lot when the scale varies. In a probabilistic a-contrario framework, a measure of meaningfulness of such an evolution through scale is derived, leading to a criterion free of parameter. Results are presented using a pair of SAR images acquired before and after the volcanic eruption of the Nyiragongo in January 2002 (Congo), showing the robustness of the method with respect to the number of false alarms.
Year
DOI
Venue
2009
10.1109/IGARSS.2009.5417327
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Keywords
Field
DocType
geophysical image processing,radar imaging,synthetic aperture radar,volcanology,AD 2002 01,CKLD,Congo,Nyiragongo volcanic eruption,a-contrario approach,cumulant-based Kullback-Leibler divergence,multiscale approach,multiscale series,synthetic aperture radar images,unsupervised change detection,Change detection,Kullback-Leibler divergence,multiscale,radar images
Computer vision,Radar imaging,Change detection,Computer science,Synthetic aperture radar,Remote sensing,A priori and a posteriori,Robustness (computer science),Lidar,Artificial intelligence,Probabilistic logic,Kullback–Leibler divergence
Conference
Volume
ISSN
ISBN
4
2153-6996
978-1-4244-3395-7
Citations 
PageRank 
References 
3
0.44
3
Authors
4
Name
Order
Citations
PageRank
Amandine Robin130.44
Grégoire Mercier260552.49
Gabriele Moser391976.92
Serpico, S.B.456048.52