By John G. Webster (Editor)
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Additional resources for 25.Electromagnetic Subsurface Remote Sensing
Also, note that in the above analysis, the only requirement for the derivation of the ﬁlter coefﬁcients is an a priori knowledge of the autocorrelation coefﬁcients of the input signal and the crosscorrelation coefﬁcients of the input signal with the desired output signal. Clearly, the ﬁlter length m needs to be speciﬁed a priori and cannot be changed during or after the computation of the ﬁlter coefﬁcients without repeating the entire computations. which means that it would be possible to recover the reﬂectivity sequence r(t).
To adapt the weights to the data, an iterative algorithm is required. The classic example is the Back Propagation (BP) algorithm (15,16). The BP algorithm minimizes the output error over all classes for a given set of training data. It achieves this by measuring the output error and adjusting the ANN’s link weights progressively backward through each layer to reduce the error. , the algorithm cannot be proven to result in a global error minimum)]. Other convergence algorithms, such as Radial Basis Functions, have been used and are faster than BP.
In other cases, however, small shifts in location need to be corrected by image registration. Multitemporal data must almost always be brought into spatial alignment using image registration, as must multiresolution data when obtained from separate sensors. Several approaches have been developed for analyzing registered multiscale/spectral/temporal data. Because most of these techniques were originally developed for analyzing multispectral image data, they will be discussed in terms of that context.
25.Electromagnetic Subsurface Remote Sensing by John G. Webster (Editor)