This paper presents a novel algorithm suitable for the lossless compression of hyperspectral imagery. The algorithm generalizes two previous algorithms, in which the concept nearest neighbor (NN) prediction implemented through lookup tables (LUTs) was introduced. Here, the set of LUTs, two or more, say M, on each band are allowed to span more than one previous band, say N bands, and the decision among one of the NM possible prediction values is based on the closeness of the value contained in the LUT to an advanced prediction, spanning N previous bands as well, provided by a top-performing scheme recently developed by the authors and featuring a classified spectral prediction. Experimental results carried out on the AVIRIS '97 dataset show improvements up to 15% over the baseline LUT-NN algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for on-board compression, since they take advantage uniquely of the sparseness of data histograms, which is originated by the on-ground calibration procedure.
Lossless compression of hyperspectral imagery via lookup tables and classified linear spectral prediction / Aiazzi B.; Baronti S.; L. Alparone. - STAMPA. - II:(2008), pp. 978-981. (Intervento presentato al convegno 2008 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) tenutosi a Boston, MA, USA nel 7-11 July 2008) [10.1109/IGARSS.2008.4779160].
Lossless compression of hyperspectral imagery via lookup tables and classified linear spectral prediction
ALPARONE, LUCIANO
2008
Abstract
This paper presents a novel algorithm suitable for the lossless compression of hyperspectral imagery. The algorithm generalizes two previous algorithms, in which the concept nearest neighbor (NN) prediction implemented through lookup tables (LUTs) was introduced. Here, the set of LUTs, two or more, say M, on each band are allowed to span more than one previous band, say N bands, and the decision among one of the NM possible prediction values is based on the closeness of the value contained in the LUT to an advanced prediction, spanning N previous bands as well, provided by a top-performing scheme recently developed by the authors and featuring a classified spectral prediction. Experimental results carried out on the AVIRIS '97 dataset show improvements up to 15% over the baseline LUT-NN algorithm. However, preliminary results carried out on raw data show that all LUT-based methods are not suitable for on-board compression, since they take advantage uniquely of the sparseness of data histograms, which is originated by the on-ground calibration procedure.
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