RS Image classification based on SVM method with texture
CHEN Bo,ZHANG You-jing,CHEN Liang (College of Water Resource and Environment,Hehai University,Nanjing 210098,China)
In order to overcome the shortage of low accuracy,the absence of pixels,spatial distribution and structure,and insufficient samples in the traditional statistical pattern recognition classification,a new method of classification using SVM based on texture is presented.In this method,the SVM classification model combined with texture analysis is established on the basis of texture extraction from Landsat7 ETM RS Image.RuYang country in Henan province is the test area.According to the model,the type of landuse in the area is classified. The classification result is compared with single data source(spectrum) SVM classification and maximum likelihood classification qualitatively and quantitatively.The research result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification;it has high spread ability toward higher array input;the overall accuracy is 90%,which increases by 6% comparing with single data source SVM and increases by 9% comparing with maximum likelihood classification and thus acquires good effectiveness.
【CateGory Index】： TP751