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.