中文摘要：

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 Laplacian Support Vector Machine,Im-proved Laplacian SVM）,首先采用分水岭算法将原始SAR图像分成多个小原型块,提取每个小原型块的图像特征作为训练样本.再采用改进的Laplacian SVM算法得到小原型块的分类结果.通过3幅SAR图像验证了提出的方法,实验表明该方法不仅提高了分割的准确性同时减少了Laplacian SVM算法用于图像分割时的运行时间.

英文摘要：

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 number of unlabeled samples is large,the required time and space complexity would be unacceptable.In order to apply it to large-scale classification problems like SAR image segmentation,a new method for SAR image segmentation named as improved Laplacian support vector machine algorithm（Improved Laplacian SVM） was proposed.Watershed algorithm was first used to decompose the original image into several small prototype blocks,and image features of each small prototype blocks were extracted as training samples.Then an improved Laplacian SVM algorithm was proposed to classify data sets.The proposed method was verified on three SAR images.The 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.