Spatio-temporal modeling based on hidden Markov model for object tracking in satellite imagery

The time series of satellite images are an important source of information for monitoring spatiotemporal changes of land surfaces. Furthermore, the number of satellite images is increasing constantly, for taking full advantage, tools dedicated to the automatic processing of information content is developed. However these techniques do not completely satisfy the geographers who exploit more currently, the data extracted from the images in their studies to predict the future. In this research we propose a generic methodology based on a hidden Markov model for analyzing and predicting changes in a sequence of satellite images. The methodology that is proposed presents two modules: a processing module which incorporating descriptors and algorithms conventionally used in image `interpretation and a learning module based on hidden Markov models. The performance of the approach is evaluated by trials of interpretation of spatiotemporal events conducted in several study sites. Results obtained allow us to analyze and to predict changes from various time series of SPOT images for observation of spatiotemporal events such as urban development. It is thus quite reasonable to use this methodology to follow the evolution of other phenomena and to predict their future states.