Abstract: In the application of multi-spectral remote sensing data,such as land cover change,environmental monitoring and thematic information extraction,the classification accuracy is not high enough due to the uncertainty of remote sen-sing information acquisition and processing.In order to further improve the classification accuracy,this paper proposed a fusion algorithm based on 6 heterogeneous machine learning classifiers.This algorithm provides classification results in abstract level,ranked level and measurement level by using prior knowledge set which is composed of precision and recall matrix,Accuracy and Difference (AD) index of the combination of classifiers,and the 3-dimensional probability matrix.Based on the Landsat 8 image data,the classification results in the study area of Beijing are forecasted by the proposed fusion algorithm and other different algorithms respectively.Experimental results shows that the 3-classifier combination composed of NB,KNN and SVM obtaines maximum AD value and the best classification effect.The abstract level output of the algorithm is 12.28% higher than the average accuracy of 6 single classifiers and even 2.24% higher than the best single classifier of SVM.Compared with the commonly used algorithms such as Random Forest (RF),Bagging and Boosting failed in the case of “strong member classifier”,the proposed fusion algorithm performs still well with accuracy 11.23%,7.56% and 11.36% higher than RF,Bagging and Boosting respectively.The proposed fusion algorithm can effectively improve the classification accuracy of remote sensing data by making full use of the diversity of classifiers and prior knowledge such as precision and recall matrix in the process of classification.

Key words: Accuracy and difference Index, Classifiers fusion, Machine learning, Prior knowledge