| 面向对象的喀斯特地区土地利用遥感分类信息提取 |
| 投稿时间：2012-11-20 点此下载全文 |
| 引用本文：李雪冬,杨广斌,李 蔓,范文娟,陈 涛.面向对象的喀斯特地区土地利用遥感分类信息提取[J].中国岩溶,2013,32(2):231-237. LI Xue dong,YANG Guang bin,LI Man,FAN Wen juan,CHEN Tao.RS classification information extraction of landuse in karst area by means of object oriented approach: A case in Bijie, Guizhou[J].Carsologica Sinica,2013,32(2):231-237. |
| 摘要点击次数: 2478 |
| 全文下载次数: 3114 |
| 学科分类: |
|
| 基金项目:贵州省重大专项项目（黔科合重大专项字\[2012]6007号）、贵州省教育厅自然科学基金项目（黔教科2007022）、贵州省工业攻关项目\[黔科合GY字（2009）3065]、贵州省科技厅科技基金项目(黔科合J字\[2008]2271号)、贵州师范大学大学生重点科研项目（自然科学项目） |
|
| 中文摘要:传统的面向像元分类方法虽然对光谱差异较为明显的遥感影像信息提取具有较好的效果，但会不可避免地产生“椒盐现象”，同时对纹理和形状信息不能充分应用，造成了大量信息损失。为了提高喀斯特地区土地利用遥感信息提取的精度，本文采用面向对象的分类方法，对贵州省毕节地区开展了土地利用遥感信息自动提取研究。首先对该地区Landsat-5 TM影像进行多尺度分割，形成影像对象层，然后综合应用基于知识决策树分类和基于样本的最邻近分类等技术对喀斯特地区进行遥感解译。结果表明，面向对象分类技术能较好地对喀斯特地区土地利用信息进行提取，同时避免了“椒盐现象”的产生，经野外采集样点数据验证，一级类分类精度为91.7 %，二级类分类精度为89.4 %，表明该方法在贵州省毕节地区应用效果良好。 |
| 中文关键词:面向对象分类 多尺度分割 隶属度函数 自动提取 |
| |
| RS classification information extraction of landuse in karst area by means of object oriented approach: A case in Bijie, Guizhou |
|
|
| Abstract:Although traditional pixel-oriented classification can get good result in the extraction of information from the remote sensing image with marked spectral difference, the “salt and pepper phenomenon” cannot be avoided and the information of texture and shape cannot be fully applied, which resulting in large amount of information loss. In this paper, in order to improve the accuracy of remote sensing information extraction, the land-used information in Bijie, Guizhou, is extracted automatically by way of object-oriented approach. Firstly, the regional images of Landsat-5 TM is segmented multiscalely to create the image object layer. Then, remote sensing interpretation is done for karst area in light of knowledge decision tree classification and Suppot Vector Machine (SVM) classification techniques. The results show that the object-oriented classification techniques can accurately and efficiently extract land-use information in karst area, and can avoid the “salt and pepper phenomenon” meanwhile. Data verification by sampling in the field proves that the first classification accuracy of the first level is 91.7 % and the second level classification accuracy is 89.4 %, indicating the object oriented approach has a good application effect in Bijie, Guizhou Province. |
| keywords:object-oriented classification multiscale segmentation membership function automatic extraction |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|