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Spatial land cover classification with the aid of neural network

A land cover classification method using a neural network was applied for the purpose of utilizing spatial information, which is expressed as a two-dimensional array of a co-occurrence matrix. The adopted neural network has three layers feed forward network architecture with back-propagation learning algorithm. In this study, the three kinds of neural network classification models were proposed. The first and the second model classifies each band image at the first stage, then performs final decision based on the first stage result. At the decision stage, arithmetic decision algorithm and second neural network are used by the first and the second model, respectively. The third model is a single stage classifier that enters all band information into the neural network for learning and classification at the same time. In order to evaluate proposed models, land cover classification using the proposed models and conventional pixel wise maximum likelihood method was conducted with Landsat TM and SPOT HRV data. As a result, the third model showed best performance, with accuracies about 4% to 6% higher than those of the classification result of the first and second model, and it showed about 17% to 27% higher than that of the maximum likelihood classification result. Finally, we examine the best performance of the neural network classification model for multitemporal remote sensing data classification, which was successful.