Self-adapted Genetic Hyperplane Classifier Algorithm for Multi-dimensional Remote Sensing Image
改进的多维遥感数据的自适应遗传超平面分类器算法

There exists a problem that is using big quantity of training data to improve classification accuracy in remote sensing supervised classification methods. In this paper, advanced improvements are proposed for the implemented genetic hyperplane algorithm to get the advantage of using smaller quantity of training data and almost the same training effect. Generally, the principle component analysis is used to acquire the 2 principle components and the result is used to classify the data. Now that the improvement is that several bands (above 3) of remote sensing data are used simultaneously for the classification. Henceforth, the information quantity that input the classifier is incremental and the technological flow is simplified. At the same time, the number of classes from the algorithm is extended, while the time consuming is not incremental. The C/C is used to implement the whole process, which involve training, classification and test. The ETM data of Beijing is given for the classification and the good performance is acquired. The result shows that it can be fully used in practical.