| 利用纹理分析方法提取TM图像信息 |
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| 引用本文： | 姜青香,刘慧平.利用纹理分析方法提取TM图像信息[J].遥感学报,2004,8(5):458-464. |
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| 作者姓名： | 姜青香 刘慧平 |
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| 作者单位： | 北京师范大学,地理学与遥感科学学院,北京,100875 |
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| 基金项目： | 教育部骨干教师资助计划资助 |
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| 摘 要： | 以北京市丰台区为试验区 ,采用纹理分析方法对高分辨率图像的纹理信息进行分析 ,选取统计指标熵 ,通过确定熵的最佳阈值 ,进行边界匹配和图像的分割 ,将光谱混淆地物菜地和耕地分割开来 ,然后将此分割结果与TM图像分类结果进行叠合 ,得到最终的分类结果。并将该结果与最大似然分类结果以及单纯依靠纹理特征得到的分类结果进行了对比。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 ,光谱与纹理特征结合得到的分类精度要远高于单纯光谱和单纯纹理的分类精度。
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| 关 键 词： | 最大似然分类法 纹理分析 灰度共生矩阵 阈值法 |
| 文章编号： | 1007-4619(2004)05-0458-07 |
| 收稿时间： | 2003/5/25 0:00:00 |
| 修稿时间： | 2003年5月25日 |
Extracting TM Image Information Using Texture Analysis |
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| JIANG Qing-xiang and LIU Hui-ping.Extracting TM Image Information Using Texture Analysis[J].Journal of Remote Sensing,2004,8(5):458-464. |
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| Authors: | JIANG Qing-xiang and LIU Hui-ping |
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| Institution: | The School of Geography,BNU,Beijing 100875,China;The School of Geography,BNU,Beijing 100875,China |
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| Abstract: | The maximum likelihood classification (MLC) is one of the most popular methods in remote sensing imag classification. Because the maximum likelihood classification is based on spectrum of objects, it cannot correctly distinguish objects that have same spectrum and cannot reach the accuracy requirement. In this paper, we take an area of Fengtai District of Beijing as an example and discuss the method of combining texture of high-resolution image with spectrum to improve the accuracy of TM image information extraction. Firstly, analysis of the textures of the in high-resolution imags is made by using texture analysis of Gray Level Coocurrence Matrices and selecting statistic index. Then threshold is selected and the optimal threshold is obtained according to entropy. Objects that have same spectrums such as vegetation land and cultivated land are distinguished using image segmentation in virtue of the optimal threshold. Finally, the find result is obtained through combining image segmentation with original classification. The finat result is compared with the classification results based on spectrum only or texture only. The result indicates that the objects with same spectrum are distinguished by using texture analysis in image classification, and the combination improves more than spectrum only or texture only in classification accuracy. |
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| Keywords: | maximum likelihood classification texture analysis Gray Level Coocurrence Matrices thresholding |
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