Remote sensed information extracting model based on multiple-objectivedecision support theory: A case study of wetland agriculture

In this paper, an optimal model of selecting the classification method in the layered classification was established based on the Analytic Hierarchical Process (AHP); and information extraction model during the layering classification was developed. In the practice of monitoring the ground surfaces, this model applied to a large amount of image processing will facilitate to reach the best synthetic effectiveness of image classification in term of the satisfaction of multiple-goal requirements of time, costs, data accuracy, and techniques applied to imagery processing. This method was applied to the classification and dynamic monitoring of the wetland agriculture in Minjiang River Estuary. Research shows that the classification model takes advantage of the visual interpretation, unsupervised classification, supervised classification, and knowledge-based classification while avoiding their shortcomings. As a result, the classification increases the efficiency in the remote-sensed data process of dynamic monitoring ground surface features, at the same time, the accuracy of classification can be met.