Spectral compression of hyperspectral images by means of nonlinear principal component analysis decorrelation

Transform-based lossy compression has a huge potential for hyperspectral (HS) data reduction. The emerging JPEG2000 technology is based on the synergistic use of both spectral and spatial compression techniques. In this context the choice of the spectral decorrelation approach can have a strong impact on the quality of the compressed image. Since hyperspectral images are highly correlated within each spectral band and in particular across neighboring frequency bands, the choice of a spectral decorrelation method that allows to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of Nonlinear Principal Component Analysis (NLPCA) transform as a lossy spectral compression method applied to hyperspectral data. Being the NLPCA the nonlinear generalization of the standard principal component analysis (PCA), it permits to represent in a lower dimensional space the same information content with less features than the standard PCA.