Two data compression algorithms intended for the compression of hyper and ultraspectral data are reviewed. These methods have been successfully applied to the compression of NASA JPL AVIRIS hyperspectral images. The two algorithms are based on slightly different requirements and assumptions. The first one is a low complexity, real-time, inter-band, least squares optimized predictor (SLSQ) whose raster-scan nature makes it amenable for on-board implementation. The second is a partitioned vector quantization algorithm (LPVQ) with tunable quality ranging from lossless to lossy. LPVQ is more complex, but it allows fast browsing and pure-pixel classification in the compressed domain, so it is more suitable to archival and distribution of compressed data. Both approaches compare well to the state-of-the-art in the compression of AVIRIS data. Preliminary results on the compression of AIRS ultraspectral sounder data are presented.
Recent years have seen a growing interest in the compression of hyperspectral imagery. In a scenario, anticipated by the NOOA for the next generation of GOES satellites, the remote acquisition platform should be able to acquire, compress, and broadcast processed data to final users, all in real time and with limited interaction with a ground station. Here we show how LPVQ, a vector quantizer algorithm previously introduced by the authors, may fit this paradigm when its arithmetic encoder is replaced with the CCSDS lossless data compressor. Beside competitive compression, this algorithm has several other interesting properties. It can be easily implemented in parallel, a number of entropy coding schemes can be used to achieve different complexity/performance tradeoffs, and the compressed stream can be used directly to perform nearest neighborhood pixel search without the need of full decompression.
We present an algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. In particular, we use a least squares optimized linear prediction method with spatial and spectral support. The performance of the predictor is competitive with the state of the art, even when the size of the prediction context is kept to a minimum; therefore the proposed method is suitable to spacecraft on-board implementation, where limited hardware and low power consumption are key requirements. With one band look-ahead capability, the overall compression of the proposed algorithm improves significantly with marginal usage of additional memory. Experiments on data cubes acquired by the NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are presented. In the second part of the paper, we revised some on-going research that aims at coupling linear prediction with polynomial fitting, exponential fitting or interpolation. Current simulations show that further improvement is possible. Furthermore, the two tier prediction allows progressive encoding and decoding. This research is promising, but still in a preliminary stage.