Since SVM is very sensitive to outliers and noises in the training set and the fuzzy feature exists in remote sensing
images, we hereby studied fuzzy support vector machine based on the affinity among samples. The fuzzy membership is
defined by not only the relation between a sample and its cluster center, but also the affinity among samples. A method
defining the affinity among samples is proposed using a sphere with minimum volume while containing maximum of the
samples. Then, the fuzzy membership is defined according to the position of samples in sphere space, which
distinguished between the valid samples and the outliers or noises. The experiment results show, it discriminates support
vectors with noise or outliers much better. Experimental results show that our method performs better than SVM in
classification of the images in Wuhan and with less influnence by the noise interference.