Compressive sensing (CS) is an emerging approach for the acquisition of signals having a
sparse or compressible representation in some basis. While the CS literature has mostly …

Hyperspectral Imaging is an essential technique to deep explore surfaces in which more
detail than the one provided by the single point spectroscopy is needed. Many devices for …

Principal component analysis (PCA) is deployed in JPEG2000 to provide spectral
decorrelation as well as spectral dimensionality reduction. The proposed scheme is …

Hyperspectral imaging is a powerful technology for remotely inferring the material properties
of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light …

Hyperspectral images present some specific characteristics that should be used by an
efficient compression system. In compression, wavelets have shown a good adaptability to a …

Hyperspectral data are a challenge for data compression. Several factors make the
constraints particularly stringent and the challenge exciting. First is the size of the data: as a …

Principal component analysis (PCA) is an effective tool for spectral decorrelation of
hyperspectral imagery, and PCAbased spectral transforms have been employed …

In the field of geophysics, huge volumes of information often need to be processed with
complex and time-consuming algorithms to better understand the nature of the data at hand …

Spectral transforms are widely used for the codification of remote-sensing imagery, with the
Karhunen-Loêve transform (KLT) and wavelets being the two most common transforms. The …

Pushbroom acquisition of hyperspectral imagery is prone to striping artifacts in the along-
track direction. A hyperspectral destriping algorithm is proposed such that the subbands of a …