1. An efficient method of data mining to facilitate ready identification of desired features within imagery data dispersed among multiple spectral bands, comprising: (a) selecting a wavelet type for use in said efficient method of data mining; (b) providing means for manipulating said data, said means at least further capable of implementing the algorithm, G ⁢ ⁢ D ⁢ ⁢ F ⁢ ⁢ I 2 ⁢ n ⁡ ( i , t ) = h o ⁢ z i + h 1 ⁢ z i + t + … ⁢ + h ( 2 ⁢ n - 1 ) ⁢ z i + ( 2 ⁢ n - 1 ) ⁢ t g o ⁢ z i + g 1 ⁢ z i + t + … ⁢ + g ( 2 ⁢ n - 1 ) ⁢ t ⁢ z i + ( 2 ⁢ n - 1 ) ⁢ t , where GDFI 2n (i, t) is a wavelet-based generalized difference feature index, i refers to wavelength band i of a data collector, t is a specified lag between wavelength bands, h 0 , h 1. .. h 2n−1 are high frequency coefficients g 0 , g 1. .. g 2n−1 are low frequency coefficients, wherein, a number of said high and low frequency coefficients is determined upon establishing an order of a wavelet of said selected wavelet type, n is a specified number of vanishing moments of said selected wavelet type, and z i , z i+t. .. z i +(2n−1)t are data necessary to yield at least one said wavelet-based generalized difference feature index from a spectral signature of an image; (c) establishing a set of wavelet-based generalized difference feature indices that may be generated later in said efficient method of data mining; (d) initiating at least one said means for manipulating data by setting a maximum wavelet order limit, selecting wavelength bands and setting K=0 and setting T=1, where K is a specified wavelet array index, and T is an incremented specified lag, defined as a specified number of said wavelength bands skipped between ones of said selected wavelength bands; (e) setting a lag limit defined as 1 ≤ t ≤ ( integer ⁡ ( m - 1 2 ⁢ n - 1 ) ) , where m is a specified number of wavelength bands in a specified dataset; (f) reading at least one said data set comprising said wavelength in said specified dataset bands into said means for manipulating; (g) identifying and discarding said specified wavelength bands having compromised data; (h) incrementing said K; (i) incrementing said T by 1; (j) computing a reduced set of difference-sum wavelength band ratios; (k) generating at least one said established wavelet-based generalized difference feature index; (l) generating a cube of each said established wavelet-based generalized difference feature index; (m) selecting at least one of said established wavelet-based generalized difference feature indices; (n) thresholding said selected pre-specified established wavelet-based generalized difference feature indices, wherein said thresholding results in only said selected pre-specified established wavelet-based generalized difference feature indices being used henceforth; (o) saving said thresholded selected pre-specified established wavelet-based generalized difference feature indices; (p) determining if said lag limit has been met; (q) if said lag limit has been met, determining if said maximum wavelet order limit has been met; (r) if said lag limit has not been met, performing another iteration of steps (h) through (r) until said lag limit has been met; (s) if said maximum wavelet order limit has been met, stopping; and (t) if said maximum wavelet order limit has not been met, setting said T=1 and performing another iteration of steps (h) through (t) until said maximum wavelet order limit has been met, wherein, if both said lag limit and said maximum wavelet order limit have been met, said efficient method of data mining is ended, resulting in an efficient identification of said desired features in said imagery data.