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Extra info for 25.Electromagnetic Subsurface Remote Sensing

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An example of remotely sensed multispectral imagery data. 35 Ȑm). INFORMATION PROCESSING FOR REMOTE SENSING sis; it can be used, in effect, to ‘‘compress’’ the data by discarding transformed bands with low variation. There are many such transformations for accomplishing this concentration of variation. One is called Principal Component Analysis (PCA) or the Principal Component Transform (PCT). Other useful transforms are the Canonical Components Transform (CCT) and the Tasseled Cap Transform (TCT).

This is the minimum-distance-to-means classifier. • Assume that all classes have the same covariance but account for correlation between bands and for different variances in each: gi (X) = (X − µi )T −1 (X − µi ) (15) The resulting decision boundary in spectral feature space is linear, but its orientation and location are dependent upon the common covariance ⌺. • Assume that classes have different covariances: gi (X) = − 12 ln | i| −1 (X i − 12 (X − µi )T − µi ) (16) The resulting decision boundary in spectral space is a second-order hypersurface whose shape and location is dependent upon the individual mean vectors Ȑi and covariance matrices ⌺i.

E. A. Robinson S. Treitel Geophysical Signal Analysis, Englewood Cliffs, NJ: Prentice-Hall, 1980. 5. C. H. Dix Seismic Prospecting for Oil, Boston: IHRDC, 1981. 6. M. A. Simaan Advances in Geophysical Data Processing, Greenwich, CT: JAI Press, 1984. 7. E. A. Robinson T. S. Durrani Geophysical Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1986. 8. O. Yilmaz Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data, Investigations in Geophysics, No. 10, Tulsa: SEG Press, 2001.

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25.Electromagnetic Subsurface Remote Sensing by John G. Webster (Editor)

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