Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images

Abstract : In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our pur-poses are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of ground truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quan-titative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.
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ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2015, special issue on Multitemporal remote sensing data analysis, 107, pp.22-37. 〈10.1016/j.isprsjprs.2015.02.006〉
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Csaba Benedek, Maha Shadaydeh, Zoltan Kato, Tamás Szirányi, Josiane Zerubia. Multilayer Markov Random Field Models for Change Detection in Optical Remote Sensing Images. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2015, special issue on Multitemporal remote sensing data analysis, 107, pp.22-37. 〈10.1016/j.isprsjprs.2015.02.006〉. 〈hal-01116609〉

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