Optical EngineeringReduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
|Format||Member Price||Non-Member Price|
|GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free.||Check Access|
We describe some applications of linear and nonlinear projection methods in order to reduce the number of spectral bands in Landsat 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 topology of the original space projection points in a reduced subspace, keeping the maximum of information. Our conclusions are: CCA is an improvement 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.