| 基于支持向量机的土地覆被遥感分类 |
| |
| 引用本文： | 田源,塔西甫拉提·特依拜,丁建丽,张飞,依力亚斯江·努尔麦麦提,韦建波.基于支持向量机的土地覆被遥感分类[J].资源科学,2008,30(8):1268-1274. |
| |
| 作者姓名： | 田源 塔西甫拉提·特依拜 丁建丽 张飞 依力亚斯江·努尔麦麦提 韦建波 |
| |
| 作者单位： | 新疆大学资源与环境科学学院，乌鲁木齐 830046;新疆大学资源与环境科学学院，乌鲁木齐 830046;新疆大学资源与环境科学学院，乌鲁木齐 830046;新疆大学资源与环境科学学院，乌鲁木齐 830046;新疆大学资源与环境科学学院，乌鲁木齐 830046;新疆大学资源与环境科学学院，乌鲁木齐 830046 |
| |
| 基金项目： | 国家自然科学基金项目(编号:40261006);新疆自治区高校科研计划项目(编号:XJEDU2004I06, XJEDU2005I07)；新疆绿洲生态重点实验室开放课题(编号: XJDX0201-2007-01,03); 新疆大学青年教师科研启动基金资助(编号:QN070122)；新疆教育厅创新研究群体基金项目(编号：XJEDU2004G04)。 |
| |
| 摘 要： | 遥感图像的分类是研究土地变化的基础。传统的遥感图像分类存在着精度不高，不确定性强的特点。本文使用支持向量机（SVM,Support Vector Machine）技术对遥感图像分类，并与传统的最大似然分类进行对比试验。结果表明不同参数组合下SVM的分类总精度和Kappa指数普遍高于最大似然分类的结果，其最高总精度高出最大似然分类0.9779%。SVM和最大似然分类结果都存在着类别混分，但是SVM混分程度远小于最大似然分类，其精度保持在可接受的范围内，如对于低密度草而言，最大似然分类的用户精度下降到84.68%，而支持向量机的用户精度虽然也有下降但还是保持在92.31%。SVM在样本数目很少的情况下表现出了出色的学习能力，是机器学习领域很有希望的一种学习方法。
|
| 关 键 词： | 支持向量机 土地覆被 遥感图像分类 |
Land Cover Classification Using Remote Sensing Image Based on Support Vector Machines |
| |
| TIAN Yuan,TASHPOLAT.Tiyip,DING Jian-li,ZHANG Fei,ILYAS.Nurmuhammat and WEI Jian-bo.Land Cover Classification Using Remote Sensing Image Based on Support Vector Machines[J].Resources Science,2008,30(8):1268-1274. |
| |
| Authors: | TIAN Yuan TASHPOLATTiyip DING Jian-li ZHANG Fei ILYASNurmuhammat and WEI Jian-bo |
| |
| Abstract: | Humans have altered land surface since ancient times, and land cover has a profound influence on global environmental change. The environmental changes (local or global) in turn affect humans. Since the 1990s, there has been more and more attention paid to the land use/cover issues that affect human society. Remote sensing is a useful tool for monitoring land cover change, and people can use the land cover maps or images to help make appropriate decisions regarding land use. We use classification of remote sensing images as a foundation for this study on land use/cover. The question of how to enhance the accuracy of image classification is a very important issue that has puzzled experts for many years. Traditional classification techniques such as maximum likelihood or isodata sometimes have a very low accuracy that restricts the results from being used as a reference for land use policy. This article uses the support vector machine (SVM) as a tool for classification of remote sensing images and compares it with traditional classification techniques such as maximum likelihood. The results indicated the SVM has a higher classification accuracy than maximum likelihood when using different parameter combinations. Its highest overall accuracy is higher than the maximum likelihood of 0.9779%. Although both techniques have a mixed-class phenomenon in the classification results, the mixing degree of SVM is lower than for maximum likelihood. Overall, the SVM classification results are in the scope which is acceptable for most applied work. Since SVM is based on statistical learning theory, its decision principle is structure risk minimization and VC dimension. When separating two classes, it guarantees the biggest interval between two classes, which means that the structure risk is minimal. Traditional classification techniques like maximum likelihood are based on mathematical statistics. This requires that remote sensing data has a normal distribution, but usually remote sensing data has a separate, multinomial distribution, so it is difficult for traditional techniques to achieve a high accuracy in image classification. SVM is a promising tool in the remote sensing image classification field. |
| |
| Keywords: | Support vector machine Land cover Remote sensing image classification |
| 本文献已被 维普 等数据库收录！ |
| 点击此处可从《资源科学》浏览原始摘要信息 |
| 点击此处可从《资源科学》下载免费的PDF全文 |
|
