Performance assessment of a recent change detection method for homogeneous and heterogeneous images

Auteurs-es

  • Jorge Prendes TéSA Laboratory, 7 boulevard de la Gare, 31500 Toulouse, France
  • Marie Chabert University of Toulouse, INP/ENSEEIHT - IRIT, 2 rue Charles Camichel, 31071 Toulouse Cedex 7, France
  • Frédéric Pascal Supélec - SONDRA, Plateau du Moulon, 3 rue Joliot-Curie, F-91192 Gif-sur-Yvette Cedex, France
  • Alain Giros CNES, 18 Av. Edouard Belin, 31401 Toulouse, France
  • Jean-Yves Tourneret University of Toulouse, INP/ENSEEIHT - IRIT, 2 rue Charles Camichel, 31071 Toulouse Cedex 7, France

DOI :

https://doi.org/10.52638/rfpt.2015.216

Mots-clés :

Remote sensing, heterogeneous images, Pléiades, SAR, change detection, similarity measure, mixture models

Résumé

A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014a,b). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization (EM) algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014a,b). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images.

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Publié-e

2015-01-29

Comment citer

Prendes, J., Chabert, M., Pascal, F., Giros, A., & Tourneret, J.-Y. (2015). Performance assessment of a recent change detection method for homogeneous and heterogeneous images. Revue Française de Photogrammétrie et de Télédétection, (209), 23–29. https://doi.org/10.52638/rfpt.2015.216

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