一种改进的Laplacian SVM的SAR图像分割算法

当有标识的样本数量有限时,Laplacian SVM算法需要加入尽量多的无标识样本,以提高分类精度.但同时当无标识样本数很大时,算法的时间和空间复杂度将难以接受.为了将Laplacian SVM应用于SAR图像分割这样的大规模分类问题中,提出了一种改进的Laplacian支持向量机算法(Improved Laplaci...

An improved Laplacian SVM algorithm for SAR image segmentation

A new method for SAR image segmentation named as improved Laplacian support vector machine algorithm (Improved Laplacian SVM) is proposed in this paper. When the number of labeled samples is limited, Laplacian SVM needs as many as possible unlabeled samples to improve the performance of classification. However, when the num of unlabeled samples is large, the time and space complexity would be difficult to accept. In order to apply it to large-scale classification problems such as image segmentation, we first use watershed algorithm to partition the original image into several small prototype blocks, and extract image features of each small prototype blocks as training samples. Then an improved Laplacian SVM algorithm is proposed to classify data sets. We verify the proposed method on texture and SAR image. And experiments show that the method not only improves the accuracy of segmentation but also greatly reduces the running time of Laplacian SVM algorithm for image segmentation.