Adaptive Resonance Theory for Classification of Remotely Sensed Image

Artificial Neural Networks (ANN) have been studied for simplified simulation to the activation of human brain and vision. Applied in understanding and interpretation of remotely sensed image, ANN can be performed more similarly with the human vision interpretability of the image in comparison with the conventional techniques such as statistical classifiers and rule-based symbolic inference. Adaptive resonance theory (ART) is developed on the basis of self-association cognitive coding theory. The major mechanism of ART is its feedback incremental learning with self-organizing structure, by which the stability and adaptability can be possessed simultaneously and the shortcomings in conventional multilayer feedforward ANN can be overcome, especially in learning phase, determination of network structure and local convergency. In this study, we firstly overview the unsupervised and supervised models of ART (including FUZZY-ART and ARTMAP), then propose a general ART-based information extraction and classification framework for remotely sensed image. With the experimental applications of land-cover classification and urban context information extraction by the presented models, the results are synthetically analyzed in comparison with conventional classifiers. The conclusion can be reached that ART model can be an alternative powerful tool for information extraction and classification of remotely sensed image especially for the features with high dimension.