1 June 2005 Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality
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Abstract
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.
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Bharath Ramakrishna, Bharath Ramakrishna, Jing Wang, Jing Wang, Antonio Plaza, Antonio Plaza, Hsuan Ren, Hsuan Ren, Chein-Chi Chang, Chein-Chi Chang, Janet L. Jensen, Janet L. Jensen, James O. Jensen, James O. Jensen, } "Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.604128; https://doi.org/10.1117/12.604128
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