LAUSR

ABSTRACT The size of remote sensing (RS) image is indeed massive due to hundreds capturing wavelengths bands used for collecting information about the ground surface. The data in the RS images are often redundant spatially, spectrally and temporally which thus give a sufficient prospect for compressing the images in different domains. In this paper, we propose a band reordering scheme to be applied on the segmented subgroup datasets of an original RS image dataset towards presenting a hybrid lossless compression technique. The proposed dataset segmentation-based band reordering process employs two heuristics, i.e. consecutive continuity limit of previous bands and continuity breakdown thresholds to be used for efficient coding of reordered dataset towards attaining better on-board compression. The aim of the proposed compression strategy is thus to increase compression gain through reordering each highly correlated bands’ segment in the RS data-cube for efficient coding. The performance of the proposed band reordering method is measured using the Consultative Committee for Space Data Systems (CCSDS) recommended lossless CCSDS 123.0-B-1 and three-dimensional (3D) Context-based Adaptive Lossless Image Codec (CALIC) predictors. The experimental results illustrate substantially higher compression of the proposed method than that of the state-of-the-art standards using various real RS hyperspectral and hyperspectral sound datasets. Quantitatively, the compression performances are increased by 0.10%–0.66% using CCSDS and 0.65–2.52% using 3D-CALIC for hyperspectral Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) datasets, 0.26%–1.00% using CCSDS and 2.16–2.57% using 3D-CALIC for hyperspectral Hyperion datasets and 4.61%–7.36% using CCSDS and 4.05%–4.88% using 3D-CALIC for hyperspectral sounder datasets by the proposed compression technique. Besides, the proposed compression scheme outperforms state-of-the-art investigated compression methods.