基于邻域最小生成树的半监督极化SAR图像分类方法

Semi-supervised PolSAR Image Classification Based on the Neighborhood Minimum Spanning Tree

该文针对极化SAR图像分类中只有少量标记样本的问题,提出了一种基于邻域最小生成树的半监督极化SAR图像分类方法。该方法针对极化SAR图像以像素为分类对象的特点,结合自训练方法的思想,利用极化SAR图像像素点的空间信息,提出了基于邻域最小生成树辅助学习的样本选择策略,增加自训练过程中被选择无标记样本的可靠性,扩充标记样本数量,训练更好的分类器。最终用训练好的分类器对极化SAR图像进行测试。对3组真实的极化SAR图像进行测试,实验结果表明,该方法在只有少量标记样本的情况下能获得满意的分类结果,且分类正确率明显优于传统的分类算法。

In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree (NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.