By John G. Webster (Editor)
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Extra info for 25.Electromagnetic Subsurface Remote Sensing
V. K. Arya J. K. Aggarwal Deconvolution of Seismic Data, Stroudsburg, PA: Hutchinson & Ross, 1982. 16. A. V. Oppenheim R. W. Schafer T. G. Stockman, Jr. Nonlinear ﬁltering of multiplied and convolved signals, Proc. IEEE, 8: 1264–1291, 1968. 17. A. V. Oppenheim R. W. Schafer Discrete-Time Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1989. 18. L. R. Rabiner R. W. Schafer Digital Processing of Speech Signals, Englewood Cliffs, NJ: Prentice-Hall, 1989. 19. T. Ulrych Application of homomorphic deconvolution to seismology, Geophysics, 36 (4): 650–660, 1971.
The Principal Component Transformation is often used to eliminate spectral band correlation. The PCT also produces a redistribution of spectral variance into fewer components, isolates spectrally uncorrelated signal components and noise, and produces features that, in some cases, align with physical variables. It is a data-dependent, linear matrix transform of the original spectral vectors into a new coordinate system that corresponds to a specific coordinate axes rotation in n-dimensions (2,5).
Exhaustive—There must be a logical and appropriate class to which to associate every pixel in the data set. 2. separable—It must be possible to discriminate accurately each class from the others in the list based on the spectral features available. 3. of informational value—The list of classes must contain the classes desired to be identified by the user. Training Phase Classification is typically carried out in two phases: the training phase and the analysis phase. During the training phase, ancillary information available to the analyst is used to define the list of classes to be used, and, from it, to determine the appropriate quantitative description of each of the classes.
25.Electromagnetic Subsurface Remote Sensing by John G. Webster (Editor)