Patent ID: 6940999
Filing Date: 2005-09-06
Classification: G06K

Abstract:
1. A method for target detection and identification, comprising the steps of: (a) receiving a hyperspectral image cube, wherein said hyperspectral image cube represents a scene in terms of wavelength and spatial position; (b) selecting a material of interest from a target database, wherein said material of interest represents a target for a target detection and identification; (c) selecting a trial pixel having a predetermined location in said hyperspectral image cube, wherein said target detection and identification is preformed on said trail pixel; (d) building a set of reference signatures which comprises a signature of said selected material of interest and a plurality of signatures of a plurality of neighboring pixels of said selected trail pixel; and (e) applying an abundance estimator to perform an abundance estimation using measurement data corresponding to said selected trail pixel and said set of reference signatures, wherein said abundance estimator is an evolutionary estimator, wherein the step (c) further comprises the steps of: (e-1) generating randomly an initial population of abundance strings by an initial population generation module that represents said abundance estimation related to said selected trail pixel of said hyperspectral image cube, wherein said abundance strings are normalized that the sum of said abundance estimation equal to 1; (e-2) performing selection and coupling in a selection and coupling module based on a cost function by a selection and coupling module, wherein said cost function is mean square error and a criteria for best abundance string is an abundance string with minimum mean square error, wherein said abundance strings with large fitness have a large number of copies in a next generation, and once said abundance strings are selected for possible use in said next generation, said abundance strings wait an action of two other operators, namely a crossover and a mutation; (e-3) generating a population of offspring by exchanging tails and heads of said abundance strings in a crossover module, wherein said crossover provides a mechanism for said abundance strings to mix and match desirable qualities thereof through a random process comprising the steps of selecting and coupling two said abundance strings by said selection and coupling module, selecting a position along the two strings uniformly at random, exchanging all characters following a crossing sit, and normalizing new reproduced abundance strings; (e-4) adding numbers, which are randomly generated, to each element of each number of said population of offspring in a mutation module; (e-5) computing a fitness value on each said abundance estimation by a fitness evaluation module, wherein a cost function takes said abundance string and returns a value, wherein said value of said cost function is then mapped into a fitness value so as to fit into said evolutionary estimator, wherein said fitness value is reward based on a performance of a possible solution represented by said abundance string, wherein said fitness values are sent to a discriminator; (e-6) performing a discrimination to determine whether to stop evolution or not, wherein a discrimination criteria is defined as a number of total evolution generations that, when said evolutionary estimator iterates to said number of total evolution generations, one of said abundance strings with highest fitness is selected as a solution and said evolutionary algorithm quit evolution, wherein a corresponding abundance estimation vector thereof is said abundance estimate of said selected trail pixel; (e-7) sending said new population of said abundance strings to said selection and coupling module; and (e-8) repeating the steps (e-2), (e-3), (e-4), (e-5), (e-6) and (e-7).