Hyperspectral Data Processing: Algorithm Design and Analysis

19.8 Conclusions

Hyperspectral data compression has been considered as a crucial step in preprocessing of hyperspectral data. Instead of focusing on design and development of 3D compression algorithms as most of current efforts are devoted to hyperspectral data compression, this chapter takes a rather different approach by addressing and investigating two important and crucial issues arising in hyperspectral data compression, subpixels and mixed pixels analysis. In particular, it shows and demonstrates via experiments several important overlooked issues that have been proven crucial in hyperspectral data compression and need to be addressed. For a hyperspectral data compression to be effective, hyperspectral data compression must be conducted on an exploitation basis and a blind use of data compression technique generally results in inappropriate interpretation. A direct application of 3D lossy compression techniques to hyperspectral imagery may cause significant loss of crucial information provided by subpixels and/or mixed pixels. Secondly, SNR and MSE have been shown inappropriate to be used as compression criteria for subpixel and mixed pixel analysis when the compression ratio is high. In other words, higher SNR or lower MSE does not guarantee better compression performance in terms of information extraction and vice versa when compression rate at low bits. Thirdly, to address the issues of subpixels and mixed pixels, spectral/spatial compression techniques are shown to be ...