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《Science Technology and Engineering》 2010-15
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Remote Sensing Image Classification Based on Support Vector Machines

ZHU Hai-zhou,JIA Yin-shan(School of Computer & Communication Engineering,Liaoning Shihua University,Fushun 113001,P.R.China)  
Support Vector Machine(SVM) is a state-of-the-art machine learning algorithm based on statistical learning theory.It tries to find the optimal classification hyperplane in high dimensional feature space to handle complicated classification and regression problems by solving optimization problems.The SVM theory applied to remote sensing image classification is still in the initial stage.Traditional algorithms used in remote sensing image classification have some problems such as low computing rate,low accuracy and much difficulty for convergence.According to SVM theory,the classification model based on SVM was constructed.By experimenting with remote sensing data and comparing the resules with others,the results indicate that the radial basis kernel function of SVM has the highest accuracy.SVM classifier has more advantages in the classification in contrast with radial basis function neural network classifier and maximum likelihood classifier.As the in-depth research on SVM continues,it will be more widely used in remote sensing image classification.
【CateGory Index】: TP751
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