Abstract: SVM has been successfully employed to solve classification of remote sensing images.Total margin-based Adaptive Fuzzy Support Vector Machine（TAFSVM） which has good quality is proposed.TAFSVM not only solves the overfitting problem resulted from the outliers with the approach of fuzzication of the penalty，but also corrects the skew of the optimal separating hyperplane dut to the very imbalanced data sets by using different cost algorithms.In addition，by introducting the total margin algorithm to replace the conventional soft margin algorithms，a lower generalization error bound can be obtained.Besides，real-valued genetic algorithms optimize its parameters.Subsequently，AGATAFSVM is used to classify the data of remote sensing images.The experimental results indicate that the proposed AGATAFSVM can achieve higher classification accuracy and is stabler than radial basis functions neural network，K-nearest neighbors classifier and standard SVM.

摘要： 支持向量机已经被成功应用于遥感图像分类。一种新型具有良好特性的支持向量机--全间隔自适应模糊支持向量机被提出。这种新型的支持向量机具有通过训练集的模糊性来增强泛化能力；对不平衡训练集具有自适应性，对正负数据采用不同的损失算法，可以提高正确分类率；通过引进全间隔算法来代替软间隔算法，可以得到更低的泛化误差等优良特性，符合遥感图像数据的内在规律。并且运用实值遗传算法对其进行参数优选，得到一种新的分类器——AGATAFSVM。最后将该分类器应用于遥感图像分类。实验结果表明，该分类器非常适用于遥感图像分类，分类精度和稳定性明显高于径向基神经网络分类器、 最近邻分类器和标准支持向量机。