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This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction. [ABSTRACT FROM AUTHOR]

In this letter, we propose a Markovian approach for interferometric synthetic aperture radar (InSAR) phase reconstruction. Recently, Markovian models based on multichannel InSAR likelihood statistics and total variation prior have been proposed to reconstruct the noisy and wrapped phase. Efficient discrete optimization algorithms based on the graph-cut technique are used to efficiently minimize the energy. Our contribution consists in extending these works to cope with continuous label sets providing more precise and accurate reconstructed profiles. The proposed approach also provides a good way to estimate local hyperparameters to adjust the prior model and preserve well discontinuities in profiles. This task is useful when working with real InSAR data where the quantization of the continuous label set leads to a loss of some physical information. The proposed method is compared to other Markovian approaches with discrete multilabel optimization algorithms. Experiments show better quality results both on simulated and real InSAR data. [ABSTRACT FROM PUBLISHER]