Hyperspectral Image Analysis Using Genetic Programming 2002

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Brian J. Ross, A.G. Gualtieri, F. Fueten, P. Budkewitsch: Hyperspectral Image Analysis Using Genetic Programming. W.B.Langdon et al.: GECCO 2002, CA, Morgan Kaufmann. 2002. pp. 1196-1203.



Genetic programming is used to evolve min- eral identification functions for hyperspec- tral images. The input image set comprises 168 images from different wavelengths rang- ing from 428 nm (visible blue) to 2507 nm (invisible shortwave in the infrared), taken over Cuprite, Nevada, with the AVIRIS hy- perspectral sensor. A composite mineral im- age indicating the overall reflectance percent- age of three minerals (alunite, kaolnite, bud- dingtonite) is used as a reference or “solu- tion” image. The training set is manually se- lected from this composite image. The task of the GP system is to evolve mineral identifiers, where each identifier is trained to identify one of the three mineral specimens. A number of different GP experiments were undertaken, which parameterized features such as thresh- olded mineral reflectance intensity and target GP language. The results are promising, es- pecially for minerals with higher reflectance thresholds (more intense concentrations).

Extended Abstract


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