Spectral Unmixing of Remote Sensing Image of the Yellow River Mouth
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摘要: 探讨了用逻辑斯蒂法进行光谱混合分解的新技术,采用黄河口TM图像进行了分析。结果证明,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。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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