(IRIT, University of Toulouse and SONDRA, CentraleSupelec, FR)
Title — Analysis of remote sensing multi-sensor heterogeneous images
Abstract — Remote sensing images are images of the Earth acquired from planes or satellites. In recent years the technology enabling this kind of images has been evolving really fast. Many different sensors have been developed to measure different properties of the earth surface, including optical images, SAR images and hyperspectral images. One of the interest of this images is the detection of changes on datasets of multitemporal images. Change detection has been thoroughly studied on the case where the dataset consist of images acquired by the same sensor. However, having to deal with datasets containing images acquired from different sensors (heterogeneous images) is becoming very common nowadays. In order to deal with heterogeneous images, we proposed a statistical model which describe the joint distribution of the pixel intensity of the images, more precisely a mixture model. On unchanged areas, we expect the parameter vector of the model to belong to a manifold related to the physical properties of the objects present on the image, while on areas presenting changes this constraint is relaxed. The distance of the model parameter to the manifold can be thus be used as a similarity measure, and the manifold can be learned using ground truth images where no changes are present. The model parameters are estimated through a collapsed Gibbs sampler using a Bayesian non parametric approach combined with a Markov random field. In this talk I will present the proposed statistical model, its parameter estimation, and the manifold learning approach. The results obtained with this method will be compared with those of other classical similarity measures.
Biography — Jorge Prendes was born in Santa Fe, Argentina in 1987. He received the 5 years Eng. degree in Electronics Engineering with honours from the Buenos Aires Institute of Technology (ITBA), Buenos Aires, Argentina in July 2010. He worked on Signal Processing at ITBA within the Applied Digital Electronics Group (GEDA) from July 2010 to September 2012. Currently he is a Ph.D. student in Signal Processing in SONDRA laboratory at Supélec, within the cooperative laboratory TéSA and the Signal and Communication Group of the Institut de Recherche en Informatique de Toulouse (IRIT). His main research interest include image processing, applied mathematics and pattern recognition.