Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality

Bharath Ramakrishna, Jing Wang, Chein I. Chang, Antonio Plaza, Hsuan Ren, Chein Chi Chang, Janet L. Jensen, James O. Jensen

Research output: Contribution to journalConference articlepeer-review

24 Scopus citations


Hyperspectral image compression can be performed by either 3-D compression or spectral/spatial compression. It has been demonstrated that due to high spectral resolution hyperspectral image compression can be more effective if compression is carried out spectrally and spatially in two separate stages. One commonly used spectral/spatial compression implements principal components analysis (PCA) or wavelet for spectral compression followed by a 2-D/3D compression technique for spatial compression. This paper presents another type of spectral/spatial compression technique, which uses Hyvarinen and Oja's Fast independent component analysis (FastICA) to perform spectral compression, while JPEG2000 is used for 2-D/3-D spatial compression. In order to determine how many independent components are required, a newly developed concept, virtual dimensionality (VD) is used. Since the VD is determined by the false alarm probability rather than the commonly used signal-to-noise ratio or mean squared error (MSE), our proposed FastICA-based spectral/spatial compression is more effective than PCA-based or wavelet-based spectral/spatial compression in data exploitation.

Original languageEnglish
Article number80
Pages (from-to)772-781
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue numberPART II
StatePublished - 2005
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI - Orlando, FL, United States
Duration: 28 Mar 20051 Apr 2005


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