Real-time onboard hyperspectral-image compression system for a parallel push broom sensor

For a dispersive hyperspectral imaging sensor, frames are continuously being generated with spatially continuous rows of differing spectral wavelengths. As the sensor advances in the direction of travel, a hyperspectral data cube can be constructed from adjacent frames. A hyperspectral sensor residing on a satellite would require either an extremely large bandwidth for the downlink or onboard data compression to transmit the majority of the data. This paper presents a compression algorithm and the implementation of the algorithm on a real-time computational architecture. The compression algorithm sues wavelet subband coding, and universal trellis code quantization. The full implementation algorithm might include differential pulse code modulation between spectral images. The computational implementation of the algorithm uses a real-time operating system and a single general-propose microprocessor upon a VME backplane. Tradeoffs between algorithm performance and computational burden are discussed. Performance of the algorithm is presented in terms root-mean-squared error and execution time. Quantitative results for the implementation of the algorithm are provided.