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