Fuzzy predictor calculation for on-board lossless compression of hyperspectral imagery by adaptive DPCM

Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Cinzia Lastri

doi:10.1117/12.514033

5 February 2004 Fuzzy predictor calculation for on-board lossless compression of hyperspectral imagery by adaptive DPCM

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Cinzia Lastri

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.514033
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

This paper investigates on the development of an advanced method for lossless compression of hyperspectral data to be implemented on board of a space platform. An adaptive Differential Pulse Code Modulation (DPCM) method, jointly exploiting spectral and spatial correlation and utilizing space-oriented context-based entropy coding, is taken as starting point. The algorithm considered utilizes a "classified" DPCM approach, in which predictors, taking into account the statistical properties of the data being compressed, are preliminarily calculated and then adaptively selected or combined. Two fuzzy clustering algorithms are tested with the aim of finding the best algorithm to be employed in the initialization phase, which is the core of the "classified" DPCM compression procedure, may be performed off line so as to unaffect the computational complexity of the online procedure running on board. The final method utilizes a standard CCSDS-Rice space encoder and represents a good tradeoff between compression capability and computational complexity. Overall coding performances, as well as differences between the two fuzzy clustering algorithms, are reported and discussed through extensive experiments carried out on four hyperspectral AVIRIS images.

Citation Download Citation

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Cinzia Lastri "Fuzzy predictor calculation for on-board lossless compression of hyperspectral imagery by adaptive DPCM", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.514033

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