The research of classification algorithm based on fuzzy clustering and neural network

Algorithms for remote sensing image classification can be cataloged into classic classification, fuzzy classification, and neural network classification. For classic classification algorithm, the space distribution of data features should be assumed, and it is difficult to put in expert knowledge to remote sensing information. In the fuzzy classification algorithm, the meaning of the subordinate degree is not definite. In the neural network classification algorithm, network framework parameters are difficult to decide, training time is long, and the neural network tends to fall into a local optimization situation. A modified Fuzzy-ISODATA algorithm and a BP neural network algorithm have been developed. The integration of the two algorithms was applied to remote sensing classification. A comparison of classification accuracy, speed and practicability for each algorithm was made based on the same training sampling area. The experiment was conducted in Shunyi, Beijing, China (40°00´-40°18´N, 116°28´-1161°58´E, which covers a total area of 1021 km²) with a TM image. The result indicates that the accuracy of integration classification algorithm increases compared with the simple fuzzy clustering algorithm and the simple neural network algorithm in the Shunyi area, but the speed should be improved.