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| 一种基于支撑向量机的遥感影像不完全监督分类新方法 | |
| 引用本文： | 刘志刚,史文中,李德仁,秦前清.一种基于支撑向量机的遥感影像不完全监督分类新方法[J].遥感学报,2005,9(4):363-373. |
| 作者姓名： | 刘志刚 史文中 李德仁 秦前清 |
| 作者单位： | 1. 北京师范大学遥感科学国家重点实验室,地理学与遥感科学学院,环境遥感与数字城市北京市重点实验室,北京,100875;武汉大学,测绘遥感信息工程国家重点实验室,湖北,武汉,430079 2. 香港理工大学,土地测量与地理咨询系,地球信息研究中心 3. 武汉大学,测绘遥感信息工程国家重点实验室,湖北,武汉,430079 |
| 基金项目： | 香港GovernmentRGC,CRC计划(3-ZB40)，香港理工大学研究基金(1.34.9709)，利用空间数据挖掘技术进行新型影像目标识别和自动分类(40023004)，国家自然科学基金项目(40176032) |
| 摘 要： | 不完全监督分类是研究在只有目标类训练样本的情况下如何准确地将目标类从数据集中提取出来。在许多遥感应用问题中,往往只需要从遥感影像中提取某一类地物。如果分类过程中只要选取目标类训练样本,将节省在训练样本选取过程中的大量人力物力。因此不完全监督分类是一个值得研究的遥感分类问题。提出了一种基于加权无标识样本支撑向量机(WUS-SVM),并在其基础发展出一种不完全监督分类方法。该方法分3个步骤:(1)在影像中随机选取一定量的无标识样本,将它们作为具有不同权重的非目标类训练样本;(2)用目标类的训练样本和加权无标识训练样本一起训练WUS-SVM,得到初步的分类器;(3)利用初步的分类器确定无标识样本的类别,并与原目标类训练样本一起再次训练SVM得到最终的分类器。通过对模拟数据和遥感影像的分类试验初步证明了该分类方法的有效性。 |
| 关 键 词： | 不完全监督分类 基于加权无标识样本的支撑向量机 支撑向量机 遥感 |
| 文章编号： | 1007-4619(2005)04-0363-11 |
| 修稿时间： | 2003年11月3日 |
Partially Supervised Classification of Remotely Sensed Imagery Using Support Vector Machines |
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| LIU Zhi-gang,SHI Wen-zhong,LI De-ren and QIN Qian-qing.Partially Supervised Classification of Remotely Sensed Imagery Using Support Vector Machines[J].Journal of Remote Sensing,2005,9(4):363-373. | |
| Authors: | LIU Zhi-gang SHI Wen-zhong LI De-ren and QIN Qian-qing |
| Abstract: | In many real world of remote sensing applications, one may be interested in identifying if the samples belong to one class from a remote sensing imagery. It is always expensive in terms of time and manpower to collect an exhaustive training sample set. Therefore, it is useful to design a classifier, by which a user needs only to collect training samples of the objective class for identifying if a pixel belongs to the class of interest or not. This is the technique of partially supervised classification. In this paper, we present an algorithm called Weighted Unlabeled Sample Support Vector Machine (WUS-SVM), based on which a new partially supervised classification method is proposed. In this method, a certain amount of unlabeled samples is first randomly selected from the test set and labeled as other classes with different weights. Second, a primary classifier is defined by WUS-SVM, in which a hyperplane is constructed to separate training samples of objective class and other classes with low weighted error and large margin width. Third, the primary classifier is used to determine the class of unlabeled samples. Thus all training samples are labeled and again they are used as training samples of support vector machines to obtain a final classifier, which is used to classify other test samples. Experimental results with both simulated and real data sets show that the proposed method is very effective. |
| Keywords: | partially supervised classification(PSC) weighed unlabeled sample support vector machine(WUS-SVM) support vector machine(SVM) remote sensing |
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