The accuracy of contextual classifier can be improved when its context distribution is properly estimated. Current methods of estimating context distribution function are "classify-and-count" method, GTGM (ground-truth-guided-method), and unbiased estimator etc..
In this thesis we suggest NSCC (NonStationary Contextual Classifier) whose context distribution is substituted for context probability which is estimated from transition probability. It is shown that by substituting context distribution for context probability the classification accuracy increases considerably much more than classical method. For estimating context probability from transition probability context probability is nonstationary in spatial domain, i.e., it varies at each pixel.
As an unsupervised classification pyramid image segmentation method is introduced and its theoretical basis is presented. The expansion of pyramid image segmentation method to two band case is studied for the multispectral image data.
Pseudo color image is made from nonstationary contextual classifier and pyramid segmentation method. And its application to edge detection and area-perimeter calculation is described.
The performances of above methods are compared with those of classical methods by experimental results.