聚类特征和SVM组合的高光谱影像半监督协同分类

本文提出了一种聚类特征和SVM组合的高光谱影像半监督协同分类方法。利用构建的协同分类框架能够将KSFCM聚类算法与半监督SVM分类器相结合，同时利用聚类和分类优势，提高分类器的分类准确率。其中，通过聚类损耗函数、分类一致函数、分类差异性、样本差异性四个指数用以构建协同分类框架，以充分利用少量类标签样本信息，避免高光谱类标签样本获取困难问题，在一定程度上解决SVM支持向量随着训练样本增加而线性增加的问题，从而寻求最佳分类结果。实验结果表明，本文所提方法得到的分类精度优于直接利用SVM进行半监督分类。

Semi-supervised Col laborative Classification for Hyperspectral Remote Sensing Image with Combination of Cluster Feature and SVM

This paper proposes a semi-supervised collaborative classification for hyperspectral remote sensing image with combination of cluster feature and SVM. The frame of our method combines kernel-spectral fuzzy C-means and semi-supervised SVM to improve the classification accuracy, through making full use of the advantages of classification and clustering. In details, ClusterLoss, ClassConsistent, classification difference and sample difference are created to build the collaborative classification frame, which can make the best of limited labeled samples and lot unlabeled data. This approach can minimize the cost of acquisition of labeled samples and in some degree solve the problem that support vector increases linearly with the number of training samples. Experimental results show that classification accuracy of the proposed method is more effective than that of semi-supervised SVM.