@Article{Benedek-etal2015,
  author =	 {Csaba Benedek and Maha Shadaydeh and Zoltan Kato and
                  Tamas Sziranyi and Josiane Zerubia},
  title =	 {Multilayer {M}arkov Random Field Models for Change
                  Detection in Optical Remote Sensing Images},
  journal =	 {ISPRS Journal of Photogrammetry and Remote Sensing},
  year =	 2015,
  volume =	 107,
  pages =	 {22--37},
  month =	 sep,
  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 purposes 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
                  quantitative 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.}
}