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30 October 2009 The hyperspectrum image identification for the point objectives based on scatter plot analysis
KaiLong Liu, XiangJun Sun, ZhiYong Zhao, Hu He, Xuetao Li
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Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74942N (2009) https://doi.org/10.1117/12.832341
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
The point objectives in the hyper-spectrum image are difficult to be identified by the geometrical figure. It is needed to reduce the dimension of the spectrum data in order to eliminate the Information superabundance of the multi-dimension hyper-spectrum image data. We develop the distributed rules of the point objectives scatter plot in the low-dimension space and confirm that the point objectives End Member are mainly distributed in the lesser confidence interval while with the higher confidence coefficient. Finally we put forward to eliminate the non-point objectives and the noise End Member going beyond the threshold so as to ensure the result of the characteristic clustering is effective. Based on the scatter plot analysis, we find the new method to extract the spectrum characteristics by which we combine the mathematics analytical models, statistical computing and the distinguishing effects tests. At the same time we establish the model of spectrum character distinguish. According to the basic characteristics of the spectrum reflection features in the green vegetation we confirm two kinds of characteristic bands, setting up the training type, and one-dimension vector is formed after sampling by linearity combination. Through the practical application, we find the rather perfect spectral classification characteristics and the discriminant function for both the point objectives and background.
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KaiLong Liu, XiangJun Sun, ZhiYong Zhao, Hu He, and Xuetao Li "The hyperspectrum image identification for the point objectives based on scatter plot analysis", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74942N (30 October 2009); https://doi.org/10.1117/12.832341
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KEYWORDS
Statistical analysis
Mathematical modeling
Vegetation
Reflectivity
Mathematics
Remote sensing
Statistical modeling
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