| 黄河口遥感图像光谱混合分解 |
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| 引用本文： | 刘庆生,刘高焕,刘素红.黄河口遥感图像光谱混合分解[J].武汉大学学报(信息科学版),2001,26(3):266-269. |
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| 作者姓名： | 刘庆生 刘高焕 刘素红 |
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| 作者单位： | 1. 中国科学院
2. 中国科学院遥感应用研究所, |
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| 摘 要： | 探讨了用逻辑斯蒂法进行了光谱混合分解的新技术，采用黄河口LM图像进行了分析。结果表明，它不仅能给出分类结果图像，而且能产生组成像元各地类的丰度图像，说明分类图像是在某种置信度下的结果。
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| 关 键 词： | 黄河口 光谱混合分析 逻辑斯蒂法 遥感图像 大气纠正 |
| 文章编号： | 1000-050X(2001)03-0266-04 |
| 修稿时间： | 2001年1月29日 |
Spectral Unmixing of
Remote Sensing Image of the Yellow River Mouth |
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| LIU Qingsheng,LIU Gaohuan,Liu Suhong.Spectral Unmixing of
Remote Sensing Image of the Yellow River Mouth[J].Geomatics and Information Science of Wuhan University,2001,26(3):266-269. |
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| Authors: | LIU Qingsheng LIU Gaohuan Liu Suhong |
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| Institution: | LIU Qingsheng 1 LIU Gaohuan 1 LIU Suhong 2 |
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| Abstract: | The spectral signature of a pixel in remotely sensed image in most cases is the result of the reflecting spectral properties of mixed land cover types constituting the area of a pixel.However,despite this phenomenon most remotely sensed image classification algorithms aim at sorting a pixel according to the spectral statistic features of a pixel.Spectral unmixing can not only give the abundance images of surface cover types constituting the area of a pixel,but also get the classification image.In this paper，we process and analyze the TM image of the Yellow River Mouth received on June 25,1999 as the following:(1) Atmospheric calibration of the image data by the internal average relative reflection,(2) Selection of the training pixels of the endmembers,(3) Spectral unmixing of the image data by the logistic model,(4) Getting the abundance image of every endmembers constituting the area of a pixel,and giving the classification image.In the end,the final image resulting from logistic model is compared qualitatively with similar products derived from maximum--likelihood classifier and spectral angle mapping technique.Then the factors effecting the classification product of logistic model are discussed.Moreover,some research aspects for the future are suggested. |
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| Keywords: | the Yellow River Mouth spectral unmixing logistic model |
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