International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
100 4
Local kappa (%)
3888882388
0+
Classes
Figure 7. Local classification accuracy
7. CONCLUSION
The purpose of this work is to design robust algorithm for
classification of remotely sensed images. Our experience
confirms that context information plays an important role in the
task of scene interpretation. At the pixel level, context information
provides neighbourhood information around a pixel, and helps to
increase the reliability of each detect object. Discrete random
fields, especially the Gibbs Random Fields (GRF) and Markov
Random Fields (MRF) provide a methodological framework
which allows the integration of context information in satellite data
classification. A powerful of these models is that the prior
probability density function modelled by the use of the contextual
information and the class conditional probability density function
modelled by the use of the observed data from one or more
sensors, can be easily combined through the use of suitable
energy function. Once the posterior energy model and the
associated parameters have been defined, pixel labelling is found
out by using the MAP estimate which is equivalent to a minimum
energy function in terms of GRF-MRF modelling. For a non-
convex energy function, the solution space may contain several
local minimum. To find a global minimum which is a truly MAP
estimate, the solution is to use an optimisation algorithm among
which ICM is the most know and used. The ICM algorithm is
sub-optimal and converges only to a local minimum of the energy
function. However, classification result of such algorithm is
acceptable and shows that the incorporation of contextual
information successfully improves classifier performances by
more than 10% in terms of global accuracy. However, algorithms
and methods to construct more complex modek and to efficiently
integrate context (context at object level which is useful for
obtaining a coherent interpretation of the whole scene) in order to
achieve higher classification accuracy, are still significant issues
worthy of further investigation.
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