To deal with the extremely high datarate and huge data volume generated aboard a hyperspectral satellite, lossless and lossy data compression techniques have been developed; these techniques can significantly reduce the amount of data onboard and on-ground. Chapters 4 and 5 of this book describe two near-lossless data compression techniques, referred to as successive approximation multistage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ), that compress hyperspectral data with a high compression ratio and restrict the compression error at the same level or even smaller than the intrinsic noise of the original data. This low-level compression error is expected to have a minor to negligible impact on ultimate applications of the data, so this kind of compression is considered to be near-lossless compression. Even so, they are still lossy compression algorithms. It is essential to assess the usability of the compressed data and to examine acceptability to users in terms of their end products and remote sensing applications. It is critical that the compression techniques preserve the information content of hyperspectral data, as a loss of information content would decrease the value of the data.
Online access to SPIE eBooks is limited to subscribing institutions.