Patent ID: 7805005
Filing Date: 2010-09-28
Classification: G06K

Abstract:
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,  where wherein, a number of said high and low frequency coefficients is determined upon establishing an order of a wavelet of said selected wavelet type, (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 (e) setting a lag limit defined as  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.