An Automatic Classification Model of Remote-Sensing Image Based on Pixel Information Decomposition Combined with Decision Tree

The authors propose a new automatic classification model of remote-sensing image using pixel information decomposition combined with decision tree in the study area of Panyu District,Guangzhou.At first,the Panyu TM image is divided into four elementary components that include water,vegetation,cement ground and soil by pixel information decomposition associated with multi-variable decision tree (four branches).Then,based on the elementary components,the authors continue to subdivide by BP neural network classification,shape index extraction and spectrum reflecting properties analysis.Finally,carry out field investigation to improve and validate the classification precision.The results show this method ensures the purity of branch objects and eliminates the disturbance and influence of unwanted objects effectively,so as to improve the classification precision.In comparison with the maximum likelihood classification by field survey data,the classification precision of this model heightens 16%.