Abstract
ABSTRACTManaging transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing 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 NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets.
Highlights
Hyperspectral (HS) sensors collect information on a very high number of wavelengths, corresponding to tens or hundreds of bands
Many of these techniques are based on decorrelation transforms, in order to exploit both spatial and spectral correlations, followed by a quantization stage and an entropy coder. In particular these approaches involve the combination of a 1-D spectral decorrelator such as the principal component analysis transform (PCA), the Discrete Wavelet Transform (DWT), or the Discrete Cosine Transform (DCT), followed by a spatial decorrelator (Abrardo, Barni, & Magli, 2010; Christophe, Mailhes, & Duhamel, 2008; Kaarna et al, 2000)
In order to reduce the loss of information derived from the dimensionality reduction, we propose the use of the Nonlinear Principal Component Analysis (NLPCA) to project the original data into a reduced dimensionality subspace by extracting meaningful components while still retaining the structure of the raw data
Summary
Hyperspectral (HS) sensors collect information on a very high number of wavelengths, corresponding to tens or hundreds of bands. In the literature several approaches have been proposed to perform the nonlinear version of PCA, among which, the most effective ones are the Nonlinear Principal Component Analysis (NLPCA) (Kramer, 1991) and the Kernel Principal Component Analysis KPCA (Scholkopf, 1998) While both techniques perform a dimensionality reduction by projecting the original data into a lower dimensional feature space, only. The image dimensionality reduction through PCA can be obtained by discarding the less relevant components in terms of variance Since this kind of approaches detect only linear correlations among spectral bands, a relevant part of the original information can be retained by the last components and lost during the compression phase (Licciardi et al 2011). To PCA, the dimensionality reduction is performed by discarding the less relevant components Both KPCA and NLPCA methods could be considered as a nonlinear generalization of the standard PCA and tend to produce similar results in terms of feature space. In Licciardi et al (2015), the NLPCA has been used to effectively remove noise from HS data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
