EFFICIENT CONTEXTUAL MEASURES FOR CLASSIFICATION OF MULTISPECTRAL IMAGE DATA (SEMANTICS, DATA COMBINATION, COMPOUND DECISION)

The most common method for labeling multispectral image data classifies each pixel entirely on the basis of the its own spectral signature. Such a method neither utilizes contextual information in the image nor does it incorporate secondary information related to the scene. This exclusion is generally due to the poor cost/performance efficiency of most contextual algorithms and a lack of knowledge concerning how to relate variables from different sources. In this research, several efficient spatial context measures are developed from different structural models for four-nearest-neighbor neighborhoods. Most of these measures rely on simple manipulations of label probabilities generated by a noncontextual classifier. They are efficient computationally and are effective in improving classification accuracy over the noncontextual result. Among other schemata, the measures include: average label probabilities in a neighborhood; label probabilities combined as a function of a metric in the label probability space; and context through semantic constraints within a Bayesian framework. In addition, an efficient implementation of a contextual classifier based on compound decision theory is developed through a simplification of the structure of the contextual prior probability. No accuracy is lost through the simplification, but computational speed is increased 15-fold. Finally, a procedure to combine label probabilities from independent data sources is proposed. A mechanism for combining the label probabilities from each of the sources as a function of their independent classification accuracies is created and evaluated