Abstract
Pattern recognition technology has had a very important role in many fields of application including image processing, computer vision, remote sensing, etc. The advent of more powerful sensor systems should enable one to extract far more detailed information than ever before from observed data, but to realize this goal requires the development of concomitant data analysis techniques which can utilize the full potential of the observed data. This report investigates classification using spatial and/or temporal contextual information. Although contextual information has been an important and powerful data analysis clue for the human-analyst, the lack of a good contextual classification scheme especially which can both use spatial and temporal context has not allowed its usefulness to be put to full use. Two different approaches to spatial-temporal contextual classification are investigated. One is based on statistical spatial-temporal contextual classification, and the other is based on decision fusion of temporal data sets which are classified individually with spatial contexts. In the first approach, a general form of maximum a posterior spatialtemporal contextual classifier is derived after spatial and temporal neighbors are defined. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. In the second approach based on ,the decision fusion, each temporal data set is separately fed into the local classifier and a final classification is performed by summarizing the local class decisions with an optimum decision fusion rule which is derived based on the minimum expected cost. The new decision fusion rule is designed to handle not only data set reliabilities but also classwise reliabilities of each data set. Experimental results with three temporal Landsat Thematic Mapper data show significant improvement of classification accuracy over non-contextual pixelwise classifier. These spatial-temporal contextual classifiers will find their use in many real applications of remote sensing, especially when the classification accuracy is important.
Date of this Version
March 1993