Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel
Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel
复制标题
使用聚类邻域核的半监督 SAR 图像变化检测
DOI：
10.1109/lgrs.2013.2295216
复制
发表时间：
2014-08-01
影响因子：
4.8
通讯作者：
An, Lin
中科院分区：
文献类型：
--
作者：
Jia, Lu;Li, Ming;An, Lin
可以以监督的方式执行变化检测。然而，合成孔径雷达（SAR）图像变化检测的监督方法可能会受到缺乏训练样本。因此，在这封信中，提出了一种基于聚类邻域（CN）核的半监督支持向量机分类器用于SAR图像变化检测。在该方法中，样本被分为两个邻域的核k均值聚类算法。此外，一个CN核构造的基础上，使用基于邻域的统计特征的复合比核。当有少量标记样本可用时，所提出的CN核探索未标记样本的信息，以增强其鉴别能力，并增强其对斑点噪声的鲁棒性。对真实的SAR图像变化检测的实验结果表明，当标记样本较少时，该方法是有效的。
Change detection can be performed in a supervised manner. However, supervised methods for synthetic aperture radar (SAR) image change detection may suffer from lack of training samples. Therefore, in this letter, a semisupervised support vector machine classifier based on a cluster-neighborhood (CN) kernel is proposed for SAR image change detection. In the proposed method, samples are categorized into two neighborhoods with kernel k-means clustering algorithm. In addition, a CN kernel is constructed based on the composite-ratio kernel using the neighborhood-based statistical features. When a few labeled samples are available, the proposed CN kernel explores the information of unlabeled samples to enhance its discriminative ability and enhance its robustness against speckles. Experimental results on real SAR image change detection demonstrate the effectiveness of the proposed method when a few labeled samples are available.