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**Sample text**

If they are not adequately distinct, the two that are the closest are combined, and the process is repeated. If they are not sufficiently compact, an additional cluster center is created within the least distinct cluster, and the process is repeated. Clustering is ordinarily not useful for final classification as such because it is unlikely that the data would be clustered into classes of specific interest. Rather it is primarily useful as an intermediate processing step. For example, in the training process, it is often used to divide the data into spectrally homogenous areas that might be useful in deciding on supervised classifier classes and subclasses and in selecting training samples for these classes and subclasses.

22) as: where the vector h = [1 − f∗ 0 − f∗ 1 .. − f∗ m−1 ] . It should be noted that this (deconvolution) approach for estimating the reﬂectivity sequence, also known as predictive deconvolution, is based on two important assumptions. , no predictable patterns) and second the wavelet must be minimum phase. This means that the amplitude spectrum of the inverse ﬁlter is the inverse of that of the seismic wavelet and the phase spectrum of the inverse ﬁlter is the negative of that of the seismic wavelet.

This is the irradiance as it would be measured at the top of Earth’s atmosphere and is referred to as the exo-atmospheric solar irradiance. The atmosphere affects the signal received by the sensor on two paths: (1) between the top of the atmosphere and Earth’s surface (solar path) and (2) between the surface and the sensor (view path). The spectral transmittance of the atmosphere Ts() along the solar path or Tv() along the view path is generally high except in prominant molecular absorption bands attributable mainly to carbon dioxide and water vapor, as illustrated in Fig.