Markov Models and Extensions for Land Cover Mapping in Aerial Imagery.

Markov models are well-established stochastic models for image analysis and processing since they allow one to take into account the contextual relationships between image pixels. In this paper, we attempt to methodically review the use of Markov models and their extensions for Land Cover mapping problem in aerial imagery according to available literature and previous research works. A new Markov model combining Markov random fields and hidden Markov models and inspired from the NSHP-HMM model, initially introduced for Handwritten Words Recognition is defined. New learning and labeling procedures are derived. [ABSTRACT FROM AUTHOR]