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| SAR和TM图像主成分变换融合中不同主分量替换的比较 | |
| 引用本文： | 杨存建,许珺,张增祥.SAR和TM图像主成分变换融合中不同主分量替换的比较[J].国土资源遥感,2001,12(3):30-35. |
| 作者姓名： | 杨存建 许珺 张增祥 |
| 作者单位： | 1. 中国科学院遥感应用研究所再生室 2. 中国科学院地理所资源与 |
| 基金项目： | 国土资源背景遥感研究的知识创新项目 (CX0 0 0 0 0 9) |
| 摘 要： | 常用的主成分变换融合方法是将一种遥感图像数据代替主成分变换后的第一主成分并进行反变换,从而得到融合信息的方法。但是,信息量较高的第一主成分被替换,往往造成一定的信息损失。本文对TM2、TM3、TM4、TM5和TM7进行主成分变换,然后用RadarsatSAR影像分别替换各主成分,并对其进行反变换。研究表明,与替换第一主成分或原始图像相比,替换第四和第五主成分的结果在信息量上有很大提高,且信息增强,类别间分离度增大,分类精度提高。但是,替换第四、第五主成分的融合结果相差不大. |
| 关 键 词： | 主成分分析 信息融合 TM SAR |
| 文章编号： | 1001-070(2001)03-0030-06 |
| 收稿时间： | 2001-01-11 |
| 修稿时间： | 2001年1月11日 |
COMPARISON OF SUBSTITUTING THE DIFFERENT PRINCIPLE COMPONENT IN THE FUSION OF TM AND SAR IMAGE PRINCIPLE COMPONENT CHANGE |
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| YANG Cun_jian ,XU Jun ,ZHANG Zeng_xiang.COMPARISON OF SUBSTITUTING THE DIFFERENT PRINCIPLE COMPONENT IN THE FUSION OF TM AND SAR IMAGE PRINCIPLE COMPONENT CHANGE[J].Remote Sensing for Land & Resources,2001,12(3):30-35. | |
| Authors: | YANG Cun_jian XU Jun ZHANG Zeng_xiang |
| Institution: | 1. Institute of Remote Sensing Applications, CAS, Beijing 100101, China; 2. LREIS, Institute of Geography, CAS, Beijing 100101, China |
| Abstract: | Principal component analysis(PCA) is one of the commonly_used methods to fuse Multisensor image data. Its procedure is to replace the first component with the high_resolution image, then to get the fused image by reversing PCA transform. Nevertheless, the first component contains more information than other components, and there will be much information lost if it is replaced. In this paper,TM2,TM3,TM4,TM5 and TM7 were analyzed by principle component transform, Radarsat SAR was used to replace the first, second, third, forth and fifth component of PCA respectively. Five different results were acquired by reversing PCA transform. The standard deviation and information entropy of five images were applied to analyze the fusion effect. The result shows that the fused images by replacing the fourth and fifth component contain more information than that by replacing the first component and can raise the separability between different types or classifications. But the difference between the fused images by replacing the fourth and the fifth component is not remarkable. |
| Keywords: | Principal component analysis(PCA) Information fusion TM SAR |
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