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| 基于分类回归树分析的遥感影像土地利用／覆被分类研究 | |
| 引用本文： | 赵萍,傅云飞,郑刘根,冯学智,B．Satyanarayana.基于分类回归树分析的遥感影像土地利用／覆被分类研究[J].遥感学报,2005,9(6):708-716. |
| 作者姓名： | 赵萍 傅云飞 郑刘根 冯学智 B．Satyanarayana |
| 作者单位： | 1. 中国科学技术大学,地球与空间科学学院,安徽,合肥,230026;安徽师范大学,国土资源与旅游学院,安徽,芜湖,241000 2. 中国科学技术大学,地球与空间科学学院,安徽,合肥,230026 3. 南京大学,城市与资源学系,江苏,南京,210093 4. Andhra大学生物系,印度,维沙卡,530003 |
| 基金项目： | 中德合作“江宁土地利用与可持续发展研究”(SILUP)项目，中国科学技术大学“面向21世纪教育振兴行动计划” |
| 摘 要： | 以专家知识和经验为基础,综合影像光谱信息和其他辅助信息进行分类的基于知识的遥感影像解译方法,是提高遥感影像分类精度,实现自动解译的有效途径之一。然而,知识的获取一直是其得以广泛应用的“瓶颈”问题。以江苏省江宁试验区土地利用/覆被分类为例,利用分类回归树分析(CART)从训练样本数据集中发现分类规则,集成遥感影像的光谱特征、纹理特征和空间分布特征进行分类实验,并与传统的监督分类和逻辑通道分类方法进行比较。结果表明,基于CART的分类方法的精度基本在80%以上,与另两种方法相比,有了较大的提高,而且该算法复杂性低,效率高。由此说明,利用CART算法构建决策树获取的分类规则是合理的。它可以快速、有效地获取大量分类规则,是促进基于知识的遥感影像分类方法在土地利用/覆被分类中广泛应用的一项有效手段。 |
| 关 键 词： | 分类回归树分析 遥感影像 土地利用/覆被分类 知识 |
| 文章编号： | 1007-4619(2005)06-0708-09 |
| 收稿时间： | 2004-06-28 |
| 修稿时间： | 2004-10-18 |
Cart-based Land Use/cover Classification of Remote Sensing Images |
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| ZHAO Ping,FU Yun-fei,ZHENG Liu-gen,FENG Xue-zhi and B. Satyanarayana.Cart-based Land Use/cover Classification of Remote Sensing Images[J].Journal of Remote Sensing,2005,9(6):708-716. | |
| Authors: | ZHAO Ping FU Yun-fei ZHENG Liu-gen FENG Xue-zhi and B Satyanarayana |
| Abstract: | Nowadays,investigations on land use / land cover change detections constitute a main objective for the global research.As a part of rapid development in technology,remote sensing has become an important tool to acquire the information of the land use/cover.Therefore,how best the extraction of timely and accurate information from these remotely sensed images is an impending problem.Recently,the knowledge-based interpretation of these images has become an effective and efficient approach to realize the automatic interpretation,which can integrate the spectral and other associated information based on experts' knowledge and experience to improve the accuracy.However,it is a bottleneck problem to obtain the knowledge for its wide application.A case study on the land use/cover classification of Jiangning study area in Jiangsu Province is discussed in the present article.At first,the data are preprocessed,then the relevant sixteen variables including geographical coordinate,grey value of four bands,textural statistics,DEM,slope and aspect are selected and extracted.The defined training sample areas are picked up by stratified random sampling techniques based on geographical coordinates.Thirdly,classification rules are discovered from these samples through Classification and Regression Tree(CART) Analysis,which integrates spectral,textural and the spatial distribution characters.Fourthly,the interpretation was performed by a judgment based on these rules.Finally,the traditional supervised as well as logic channel classifications are also performed to check the classification accuracies.The results have suggested that the accuracy of classification based on the CART is higher than others',which can obtain a lot of reasonable rules most quickly and effectively.So,it was felt that it is a good way to promote the wide application of knowledge-based interpretation of remote sensing images. |
| Keywords: | Classification) and Regression(Tree(CART)) Analysis remote sensing land use/cover classification knowledge |
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