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

• Published in: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5367 - 5371
• Main Authors: Prendes, Jorge, Chabert, Marie, Pascal, Frederic, Giros, Alain, Tourneret, Jean-Yves
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2014
• Subjects: Adaptive optics; change detection; Image analysis; Image sensors; Joints; mixture models; multi-temporal images; optical images; Optical imaging; Optical sensors; remote sensing; Synthetic aperture radar
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2014.6854628

This paper introduces a new statistical model for homogeneous images acquired by the same kind of sensor (e.g., two optical images) and heterogeneous images acquired by different sensors (e.g., optical and synthetic aperture radar (SAR) images). The proposed model assumes that each image pixel is distributed according to a mixture of multi-dimensional distributions depending on the noise properties and on the transformation between the actual scene and the image intensities. The parameters of this new model can be estimated by the classical expectation-maximization algorithm. The estimated parameters are finally used to learn the relationships between the different images. This information can be used in many image processing applications, particularly those requiring a similarity measure (e.g., change detection or registration). Simulation results on synthetic and real images show the potential of the proposed model. A brief application to change detection between optical and SAR images is finally investigated.