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Extra info for 25.Electromagnetic Subsurface Remote Sensing
This labeling process can be carried out directly on the remotely sensed image data, on image features derived from the original image data (such as band ratios or data transforms), or on combinations of the original image data and derived features. Whatever the origin of the data, the classification feature space is the n-dimensional vector space spanned by the data vectors formed at each image pixel. The two main types of image classification are unsupervised and supervised. In unsupervised classification, an analysis procedure is used to find natural divisions, or clusters, in the image feature space.
Spectral ratios are also useful in the analysis of image data containing significant amounts of topographic shading. The process of spectral ratioing tends to reduce the effect of this shading. The data contained in each band of multispectral image data are often correlated with the data from some of the other bands. When desirable to do so, this correlation can be reduced by transforming the data in such a way that most of the data variation is concentrated in just a few transformed bands. Reducing the number of image bands in this way not only may make the information content more apparent but also serves to reduce the computation time required for analy- (a) (b) (c) (d) Figure 1.
Similar problems will also arise even if at some frequencies the values of the amplitude spectrum of w(t) are very small. Clearly, using Eq. (33) for calculating the inverse ﬁlter f(t) is not feasible in almost all realistic applications. Suppose instead, the Wiener ﬁltering approach is used and a ﬁlter f(t) is designed which, when applied to the source waveform w(t), will produce an output which is as close as possible to a desired impulse function (a spike). In other words, referring to the Wiener Filtering approach discussed earlier, suppose the source waveform has n samples, w0 , w1 , .
25.Electromagnetic Subsurface Remote Sensing by John G. Webster (Editor)