A multidisciplinary user acceptability study of hyperspectral data compressed using an on-board near lossless vector quantization algorithm.

To deal with the extremely high data rate and huge data volume generated onboard a hyperspectral satellite, the Canadian Space Agency (CSA) has developed two fast on-board data compression techniques for hyperspectral imagery. The CSA is planning to place a data compressor on-board a proposed Canadian hyperspectral satellite using these techniques to reduce the requirement for onboard storage and provide a better match to available downlink capacity. Since the compression techniques are lossy, it is essential to assess the usability of the compressed data and the impact on remote sensing applications. In this paper. 11 hyperspectral data users covering a wide range of application areas and a variety of hyperspectral sensors assessed the usability of the compressed data using their well understood datasets and predefined evaluation criteria. Double blind testing was adopted to eliminate bias in the evaluation. Four users had ground truth available. They qualitatively and quantitatively compared the products derived from the compressed data to the ground truth at compression ratios from 10:1 to 50:1 to examine whether the compressed data provided the same amount of information as the original for their applications. They accepted all the compressed data. The users who did not have ground truths available evaluated the compression impact by comparing the products derived from the compressed data with those derived from the original data. They accepted most of the compressed data. [ABSTRACT FROM AUTHOR]