Remote Sensing Imagery Classification Based on Multiple Classifiers Combination Algorithm

A new multiple classifiers combination algorithm based on the theories of information relations is proposed,it can dynamically adjust the weights of different classifiers.The new algorithm is used to combine decision tree algorithm,BP,and SVM.The experimental results of TM image of Changsha city in China show that:① There are significant difference among the results of three algorithms,the proportion of pixels with different classes in water area is 15.12%,and the proportion of pixels with different classes in building area is 54.93%;② Three algorithms get high classification precision and every classifier has different advantages,decision tree algorithm can distinguish the water area,and building area,BP algorithm can distinguish water area,and wood area and SVM algorithm can distinguish water area,wood area and building area;③ The algorithm proposed in this paper has the highest precision,the total precision is 85.71%,and the Kappa coefficient is 80.56%.