A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available a...

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Veröffentlicht in:IEEE transactions on image processing 2015-03, Vol.24 (3), p.799-812
Hauptverfasser: Prendes, Jorge, Chabert, Marie, Pascal, Frederic, Giros, Alain, Tourneret, Jean-Yves
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Chabert, Marie
Pascal, Frederic
Giros, Alain
Tourneret, Jean-Yves
description Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.
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subjects Adaptive optics
change detection
Computer Science
EM algorithm
Engineering Sciences
Image Processing
Image sensors
manifold learning
mixture models
Networking and Internet Architecture
Noise
Optical images
Optical imaging
Optical sensors
SAR images
Signal and Image processing
Synthetic aperture radar
title A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors
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