At present neural network models make great progress in remote sensing image classification, but these models have some serious limitations, such as easily getting stuck at a local minimum, converging too slowly and uneasy-fixed network structure. Support vector machines (SVMs) is a nonlinear mapping algorithm based on the Statistical Learning Theory, and developed over the last three decades by Vapnik, Chervonenkis et al. It gained extensive applications in pattern recognition and regression analysis etc. Compared the SVM algorithm with neural network models, the former is
based on self-contained mathematics theory, and furthermore solves a global optimization problem and makes sure the result is not local minimum. These enable the SVM algorithm excellent classification performance. The paper proposed a new hybrid classification method that combines support vector machines with fuzzy membership
function for remote sensing image. Firstly the method constructs multi-class Support Vector Machines classifier for remote sensing image, and discusses parameter estimation problem, and then uses RBF kernel SVM to classify whole remote sensing image. Secondly aiming at the disadvantage of SVM classifier that exists some mixed samples (one
sample divided into two or more categories) and missed samples (one sample is not classified), and using fuzzy membership function method to reclassify these mixed and missed samples. Experimental results suggested the accuracy of this hybrid classifier is higher than single SVM method, or single fuzzy membership function decision method or BP
neural network model.