Temporal change enhancement in multispectral images remotely sensed from satellites | Zendy

Bill P. Pfaff | Zendy; Doran J. Baker | Zendy; L.G. Allred | Zendy; Gene A. Ware | Zendy

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<title>Temporal change enhancement in multispectral images remotely sensed from satellites</title>

Author(s) -

Bill P. Pfaff,

Doran J. Baker,

L.G. Allred,

Gene A. Ware

Publication year - 1997

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.280611

Subject(s) - multispectral image , principal component analysis , computer science , satellite , artificial intelligence , software , remote sensing , multispectral pattern recognition , principal (computer security) , pattern recognition (psychology) , computer vision , geography , engineering , programming language , aerospace engineering , operating system

The application of principal components analysis (PCA) to multispectral satellite images is a routine way to present the data in false-color composite images. These composite images include a very high percentage of available information and have no correlation between the displayed colors. PCA routines are included in commercial GIS software, and custom algorithms are in wide use.This paper describes an early application of a new, genetic algorithm based, PCA routine. Landsat data for an Idaho farm were evaluated for temporal changes using this new algorithm, and the eigenvalues consistently converged with excellent results.

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