Abstract: Traditional classifier is made up of both positive and negative data. It is a common situation in remote sensing image classification：users are only interested in one specific land-cover type. However, labeling land-cover is a time consuming and labor intensive process, and unlabeled data are usually obtained easily and contain useful information. For this reason, a remote sensing image classification method based on Positive and Unlabeled data（PUL） is proposed. Firstly, according to the inherent characteristics of positive data and combined with support vector data description confident positive and negative samples can be extracted from unlabeled data, and those examples are eliminated from unlabeled set. Then it uses above extracted samples to train a SVM classifier and extract relative confident positive and negative sample from unlabeled set again. The extraction rule is based on the performance of unlabeled set in the SVM classifier. The last step is weighted SVM process. The weight of initial positive and negative samples is 1. The weight of samples extracted by SVM classifier is between 0 and 1. To verify the effectiveness of PUL method, it does classification experiment in remote sensing image and is compared with One-Class SVM（OC-SVM）, Gauss Data Description（GDD）, Support Vector Data Description（SVDD）, Biased SVM and Multi-class SVM. The results show that PUL is helpful to the improvement of classification and better than above OC-SVM methods and Multi-class SVM.

Key words: Biased Support Vector Machine（SVM）, Support Vector Data Description（SVDD）, Gauss Data Description（GDD）, One-Class SVM（OC-SVM）, remote sensing image classification, Multi-class SVM

摘要： 传统分类器的构建需要正样本和负样本两类数据。在遥感影像分类中，常出现这样一类情形：感兴趣的地物只有一种。由于标记样本耗时耗力，未标记样本往往容易获取并且包含有用信息，鉴于此，提出了一种基于正样本和未标记样本的遥感图像分类方法（PUL）。首先，根据正样本固有特征并结合支持向量数据描述（SVDD）从未标记集筛选出可信正负样本，再将其从未标记集中剔除；接着将其带入SVM训练，根据未标记集在分类器中的表现设立阈值，再从未标记集中筛选出相对可靠的正负样本；最后是加权SVM（Weighted SVM）过程，初始正样本及提取出的可靠正负样本权重为1，SVM训练筛选出的样本权重范围0~1。为验证PUL的有效性，在遥感影像进行分类实验，并与单类支持向量机（OC-SVM）、高斯数据描述（GDD）、支持向量数据描述（SVDD）、有偏SVM（Biased SVM）以及多类SVM分类对比，实验结果表明PUL提高了分类效果，优于上述单类分类方法及多类SVM方法。

关键词: 有偏SVM, 支持向量数据描述, 高斯数据描述, 单类支持向量机, 遥感图像分类, 多类SVM