Reduction of the number of spectral bands in LANDSAT images with projection methods: pertinence of the resulting information

This paper describes applications of linear and nonlinear projection methods, in order to obtain a reduction of the number of spectral bands in LANDSAT multispectral images. We present Curvilinear Component Analysis (CCA, nonlinear method) and an optimisation of it based on the use of Principal Component Analysis (PCA, linear method). In order to evaluate the pertinence of the information kept by each transformation, we then apply segmentation on the transformed and original images. This processing allows us to show that the structure (the landscape organization) of the image is preserved by each transformation. This paper tends to show several results : CCA is an improvement for dimensions reduction of multispectral images ; CCA is really a nonlinear extension of PCA ; CCA optimisation through PCA (called CCAinitPCA) allows a reduction of the calculations, providing a result identical to that of CCA.