A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing ImagesReport as inadecuate




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1 TéSA - Telecommunications for Space ant Aeronautics 2 IRIT - Institut de recherche en informatique de Toulouse 3 L2S - Laboratoire des signaux et systèmes 4 CNES - Centre National d-Etudes Spatiales

Abstract : In recent years, remote sensing of the Earth surface using images acquired from aircraft or satellites has gained a lot of attention. The acquisition technology has been evolving fast and, as a consequence, many different kinds of sensors e.g., optical, radar, multispectral, and hyperspectral are now available to capture different features of the observed scene. One of the main objectivesof remote sensing is to monitor changes on the Earth surface. Change detection has been thoroughly studied in the case of images acquired by the same sensors mainly optical or radar sensors. However, due to the diversity and complementarity of the images, change detection between images acquired with different kinds of sensors sometimes referred to as heterogeneous sensors is clearly aninteresting problem. A statistical model and a change detection strategy were recently introduced inJ. Prendes, M. Chabert, F. Pascal, A. Giros, and J.-Y. Tourneret, Proceedings of the IEEE Inter-national Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014; IEEE Trans.Image Process., 24 2015, pp. 799-812 to deal with images captured by heterogeneous sensors. The main idea of the suggested strategy was to model the objects contained in an analysis window by mixtures of distributions. The manifold defined by these mixtures was then learned using trainingdata belonging to unchanged areas. The changes were finally detected by thresholding an appropriate distance to the estimated manifold. This paper goes a step further by introducing a Bayesian nonparametric framework allowing us to deal with an unknown number of objects in analysis windows without specifying an upper bound for this number. A Markov random field is also introduced to account for the spatial correlation between neighboring pixels. The proposed change detector is validated using different sets of synthetic and real images including pairs of optical images and pairs of optical and radar images showing a significant improvement when compared to existingalgorithms.

Keywords : Gibbs sampler Change detection Bayesian nonparametric Markov random field Optical images Synthetic aperture radar images





Author: Jorge Prendes - Marie Chabert - Frédéric Pascal - Alain Giros - Jean-Yves Tourneret -

Source: https://hal.archives-ouvertes.fr/



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