资料下载
×
基于多源信息的TM遥感图像计算机分类
消耗积分:3 |
格式:rar |
大小:537 |
2009-07-16
417804
分享资料个
本文以榆林市城区及其周边范围为实验区，以TM遥感图像的第一主成分纹理信息、归
一化植被指数和MNF变换得到的四个波段为数据源，采用支持向量机方法进行分类，并与最大似然法分类和单纯利用光谱信息的基于SVM分类结果进行比较。试验结果表明，将纹理分析方法应用于图像分类中可区分光谱混淆的地类；和传统的分类方法相比，采用支持向量机技术，使用光谱与纹理特征结合的分类方法可以获得更高的分类精度。
关键词：最小噪声分离变换 灰度共生矩阵 支持向量机
Abstract: This paper discusses about the extraction of classification information of Yulin city and nearby area from an TM image and deals with the image classification based on the SVM method integrating the information of texture of PCA1, NDVI and MNF. And comparing to the results based on Maximum Likelihood and SVM method with single-source data. The results showed that the objects with same spectrum are distinguished by using texture analysis in image classification。 Compare with the traditional classification method, the classification based on the information of texture and spectrum using SVM could acquire higher classifying precision.
Key words: minimum noise fraction, gray level co-occurrence matrix, support vector machine
一化植被指数和MNF变换得到的四个波段为数据源，采用支持向量机方法进行分类，并与最大似然法分类和单纯利用光谱信息的基于SVM分类结果进行比较。试验结果表明，将纹理分析方法应用于图像分类中可区分光谱混淆的地类；和传统的分类方法相比，采用支持向量机技术，使用光谱与纹理特征结合的分类方法可以获得更高的分类精度。
关键词：最小噪声分离变换 灰度共生矩阵 支持向量机
Abstract: This paper discusses about the extraction of classification information of Yulin city and nearby area from an TM image and deals with the image classification based on the SVM method integrating the information of texture of PCA1, NDVI and MNF. And comparing to the results based on Maximum Likelihood and SVM method with single-source data. The results showed that the objects with same spectrum are distinguished by using texture analysis in image classification。 Compare with the traditional classification method, the classification based on the information of texture and spectrum using SVM could acquire higher classifying precision.
Key words: minimum noise fraction, gray level co-occurrence matrix, support vector machine
声明：本文内容及配图由入驻作者撰写或者入驻合作网站授权转载。文章观点仅代表作者本人，不代表电子发烧友网立场。文章及其配图仅供工程师学习之用，如有内容侵权或者其他违规问题，请联系本站处理。 举报投诉
评论(0)
发评论
- 相关下载
- 相关文章
