TITLE:
Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain
AUTHORS:
Houcine Essid, Imed Riadh Farah, Vincent Barra
KEYWORDS:
Satellite Image; Remote Sensing; Hidden Markov Model; Change Detection; Image Processing
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.3,
September
12,
2013
ABSTRACT:
Nowadays
remote sensing is an important technique for observing Earth surface applied to
different areas such as, land use, urban planning, remote monitoring, real time
deformation of the soil that can be associated with earthquakes or landslides,
the variations in thickness of the glaciers, the measurement of volume changes
in the case of volcanic eruptions, deforestation, etc. To follow the evolution
of these phenomena and to predict their future states, many approaches have
been proposed. However, these approaches do not respond completely to the
specialists who process yet more commonly the data extracted from the images in
their studies to predict the future. In this paper, we propose an innovative
methodology based on hidden Markov models (HMM). Our approach exploits temporal
series of satellite images in order to predict spatio-temporal phenomena. It
uses HMM for representing and making prediction concerning any objects in a satellite image. The first
step builds a set of feature vectors gathering the available information. The
next step uses a Baum-Welch learning algorithm on these vectors for detecting
state changes. Finally, the system interprets these changes to make
predictions. The performance of our approach is evaluated by tests of
space-time interpretation of events conducted over two study sites, using
different time series of SPOT images and application to the change in
vegetation with LANDSAT images.