Several linear transforms with constructions more general than that of principal component analysis are considered for spectral decorrelation in the compression of hyperspectral imagery. Specifically, orthogonal nonnegative matrix factorization, generalized principal component analysis, and principal component analysis coupled with explicit segmentation based on spectral angle mapping are considered. These spectral- decorrelation techniques are employed in conjunction with wavelet-based spatial decorrelation for hyperspectral compression using a 3D version of the well-known SPIHT algorithm. A shape-adaptive wavelet transform and shape-adaptive SPIHT coder are used in the case of the latter two spectral-decorrelation techniques which segment the hyperspectral dataset into multiple distinct pixel classes. Experimental results reveal that, despite their general formulation, the proposed techniques fail to offer spectral-decorrelation performance superior to that of traditional principal component analysis.
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