The amount of remotely sensed data increases rapidly

and the information contained in this data becomes more and more complicated

the way how to classify these datasets generalized and effectively is a problem which needs urgently to be solved.A modified rotation forest algorithm is proposed which takes the RBFNN as the base classifier to classify the remote sensing image.The input training dataset is changed by the rotation forest which can output a much small sub-feature.Then the non-redundancy feature set is got by using PCA technology to process these new sub-features.Finally

the training dataset changes according to the coefficient by the PCA transformation.This change will lead a higher diversity factor among these sub-classifiers which will give a much higher accuracy.The proposed method can obtain higher classification accuracy than other traditional methods when it used on the Zhalong wetland remote sensing image

and this algorithm has much higher generalization ability and much less over study phenomenon.