Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images

Marc Lennon; Gregoire Mercier; Marie-Catherine Mouchot; Laurence Hubert-Moy

doi:10.1117/12.454150

28 January 2002 Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images

Marc Lennon, Gregoire Mercier, Marie-Catherine Mouchot, Laurence Hubert-Moy

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454150
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

This paper presents a multidimensional data nonlinear projection method applied to the dimensionality reduction of hyperspectral images. The method, called Curvilinear Component Analysis (CCA) consists in reproducing at best the topology of the joint distribution of the data in a projection subspace whose dimension is lower than the dimension of the initial space, thus preserving a maximum amount of information. The Curvilinear Distance Analysis (CDA) is an improvement of the CCA that allows data including high nonlinearities to be projected. Its interest for reducing the dimension of hyperspectral images is shown. The results are presented on real hyperspectral images and compared with usual linear projection methods.

Citation Download Citation

Marc Lennon, Gregoire Mercier, Marie-Catherine Mouchot, and Laurence Hubert-Moy "Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454150

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