Proposed Model To Measure The Effect Of Discontinuity Adaptive mrf Models In Fuzzy Based Clasiifier On Satellite Images.

Rakesh Dwivedi, S. K. Ghosh And Anil Kumar.


Presently wide ranges of remotely sensed data are available from earth observation satellites. This data are analyzed to prepare land use/ land cover maps using different remote sensing techniques. Image classification is one way to produce these land use/ land cover maps. Due to continuous nature of real world phenomena, the image classification to map land cover classes is a challenge. Presence of mixed pixels decreases the efficiency of image classification. Fuzzy classification technique such as Fuzzy c-Means (FCM) can be used to handle mixed pixels. Although FCM has the advantage of classifying mixed pixels by assigning membership value, it does not incorporate spatial contextual information of the pixels into its classifying algorithm. Use of context eliminates the problem of isolated pixels and improves the classification accuracy. In this research work a contextual FCM classifier has to be developed by using MRF models. Smoothness prior and four discontinuity adaptive prior have been used to incorporate contextual information with FCM. The developed discontinuity adaptive contextual FCM classifier would be tested both on coarse and fine resolution dataset i.e. AWFIS and LISS-III with spatial resolution 60 m and 20m respectively. It is expected that the discontinuity adaptive prior models, improves the overall classification accuracy by preserving the edges at boundaries and the classified output is consistent with spectrally and spatially

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