Skip to Main content Skip to Navigation
Journal articles

Monitoring intra-urban changes with Hidden Markov Models using the spatial relationships

Abstract : This paper presents a methodology for integrating a new parameter measuring spatial relationships in the hidden Markov models (HMM) in order to detect, interpret and predict changes in urban areas from satellite images. This approach is divided into three phases: the detection of different spatial relationships in the urban area; the training of a hidden Markov model using Baum-Welch learning algorithm, integrating the changing spatial relationships obtained through the Allen's temporal algebra; the interpretation of changes in urban area and the prediction of future changes. Simulated spatiotemporal changes on synthetic data show the interest of this method for the analysis of spatiotemporal changes of relations between objects. Results allows detection and prediction to be performed from the various time series of images for the observations of spatiotemporal events such as urban expansion. It is therefore reasonable to use this approach to interpret and estimate the movement of the urban area.
Document type :
Journal articles
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-00794297
Contributor : Bibliothèque Télécom Bretagne Connect in order to contact the contributor
Submitted on : Monday, February 25, 2013 - 2:33:57 PM
Last modification on : Wednesday, April 21, 2021 - 8:52:05 AM

Identifiers

  • HAL Id : hal-00794297, version 1

Citation

Houcine Essid, Imed Riadh Farah, Akrem Sellami, Vincent Barra. Monitoring intra-urban changes with Hidden Markov Models using the spatial relationships. International Journal on Graphics, Vision and Image Processing, 2012, 12 (1), pp.49-55. ⟨hal-00794297⟩

Share

Metrics

Record views

296