6533b85afe1ef96bd12b8b1c
RESEARCH PRODUCT
Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
Ludovic JournauxPierre GoutonIrène Foucherotsubject
Computer sciencebusiness.industryDimensionality reductionQuantization (signal processing)Multispectral imageGeneral EngineeringImage processingPattern recognitionImage segmentationSpectral bandsNonlinear Sciences::Cellular Automata and Lattice GasesAtomic and Molecular Physics and OpticsStatistics::Machine LearningComputer Science::Computer Vision and Pattern RecognitionPrincipal component analysisComputer visionArtificial intelligenceProjection (set theory)businessSubspace topologydescription
We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitPCA allows a reduction of the computation burden but provides a result identical to that of CCA. © 2006 Society of Photo-Optical Instrumentation
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-06-01 | Optical Engineering |