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A multivariate statistical model for multiple images acquired by homogeneous or heterogeneous sensors

Abstract : 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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https://hal-supelec.archives-ouvertes.fr/hal-01104184
Contributor : Virginie Bouvier <>
Submitted on : Friday, January 16, 2015 - 12:05:34 PM
Last modification on : Tuesday, October 20, 2020 - 10:32:04 AM

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Jorge Prendes, Marie Chabert, Frederic Pascal, Alain Giros, Jean-Yves Tourneret. A multivariate statistical model for multiple images acquired by homogeneous or heterogeneous sensors. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. ⟨10.1109/ICASSP.2014.6854628⟩. ⟨hal-01104184⟩

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