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20 October 1993 Pattern-recognition method of multispectral remote sensing for suspended matter in coastal water
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Simple and successful methods have been presented for deriving suspended matter concentration from optical reflectance spectra of seawater. The pattern recognition method allows multispectral radiance to be analyzed for signatures characteristic of different, independently vary phenomena and can be used to evaluate the information content. Landsat MSS data on Nov. 28, 1983 and quasi-synchronous field data at the mouth of Yellow River have been used in this paper. By pattern recognition, the first three characteristic vectors which are derived from the radiance covariance matrix are enough for this study. These vectors accounted for 98.1, 98.9, 99.2 percent respectively. The first vector is mainly loaded by visible light channels, the optical properties of suspended matter enhance the backscattered radiance in these channels. The scalar multiplier for the first vector represents the first vector weighted in each station. We get the regression equation from a least-square fit between the scalar multiplier and the logarithm of the field data observed at each station. The correlation coefficient is 0.81, which is much higher than the correlation coefficient between the radiance of single channel and the logarithm of suspended matter concentration (0.59, 0.70, 0.72, 0.37 respectively). Using the model, we get the information of concentration and distribution of suspended matter from Landsat data on Nov. 28, 1983, Oct. 5 1984, Dec. 3 1988. These results confirmed with hydrology and meteorology information were satisfactory.
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Jun Ma, Xiao-Sheng Huang, Tao Huang, and Zhishen Liu "Pattern-recognition method of multispectral remote sensing for suspended matter in coastal water", Proc. SPIE 2028, Applications of Digital Image Processing XVI, (20 October 1993);

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