作为支持向量机(support vector machine, SVM)高光谱影像分类的一个重要环节, 参数设置的效率和精度直接影响到SVM模型训练效率和最终分类精度。 本文首先建立一个SVM高光谱影像分类器, 提出了利用免疫克隆选择算法优化的交叉验证进行核函数参数和惩罚因子C的优化选择的方法, 得到了一种基于克隆选择优化的支持向量机(clonal selection SVM, CSSVM)高光谱影像分类器。 然后将CSSVM与传统的基于网格搜索交叉验证的支持向量机（gird search SVM, GSSVM）分类器进行了对比评价, 评价指标包括模型训练时间和分类精度等。 最后基于AVIRIS高光谱遥感影像进行了两算法分类对比试验, 结果表明: 提出的CSSVM测试样本总分类精度超过85.1%和Kappa系数超过0.821 3, 影像总分类精度超过81.58%和Kappa系数超过0.772 8, CSSVM与GSSVM的分类精度差别在0.08%以内, Kappa系数差别在0.001以内; CSSVM的模型训练时间是GSSVM的1/6至1/10, 得到显著缩短; CSSVM方法在保持传统GSSVM优良分类精度的基础上, 极大提高了模型的训练效率。

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter σ and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.821 3 and 0.772 8, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.