基于流形主动学习的遥感图像分类算法

Remote sensing image classification based on active learning with manifold structure

为了高效地解决遥感图像分类问题,提出一种基于流形学习和支持向量机(SVM)的图像分类算法。在初始阶段,该算法首先利用初始训练集训练SVM,并且使用SVM找出离分类界面最近的样本;然后在所选样本中利用拉普拉斯图构建样本空间的流形结构,选出最具有代表性的样本加入训练集;最后利用高光谱图像进行实验进行验证。通过与现有的主动学习算法进行比较,结果表明该算法获得了更高的分类准确率。

To efficiently solve remote sensing image classification problem, a new classification algorithm based on manifold structure and Support Vector Machine （SVM） was proposed. Firstly, the proposed algorithm trained the SVM with initial training set and found the samples close to the decision hyperplane, then built the manifold structure of the samples by using Laplacian graph of the selected samples. The manifold structure was applied to find the representative samples for the classifier. The experimental evaluations were conducted on the hyperspectral images, and the effectiveness of the proposed algorithm was evaluated by comparing it with other active learning techniques exiting in the literature. The experimental results on data set confirm that the algorithm has higher classification accuracy.