Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

Abstract

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

Comprehensive review of hyperspectral image compression algorithms

“…Some state-of-the-art algorithms are 3D-DPCM, 33 superpixel-based segmentation-CRLS, 34 RLS-adaptice length prediction, 35 and LMS-APL. 36 Technique. Prediction-based technique is easy to implement on HSIs and can be easily explained by Fig.…”

“…Multitemporal compression 81 is obtained by enhancing the 3-D prediction-based technique to 4-D prediction based for temporal decorrelation. Lossless compression is expected in 4-D images as these are processed and used by automated programs running on the computer.…”

“…DWT spectral transforms are applied on the images to improve the compression performance by reducing the number of bands and spatial data to be coded. Shen et al 36 proposed an adaptive learning-based compression of multitemporal HSIs. It used correntropy least mean square (CLMS) algorithm for prediction of pixels on the basis of already predicted spectral and temporal information.…”

“…It ensures lossless compression by coding the prediction error using Golomb rice coding and arithmetic coding. The method was further improved with the help of fast-lossless-4D predictor 81 in which pixels are predicted by using a linear combination of neighboring pixels in all four dimensions. By subtracting data with their local mean in each band and then for each pixel in a band in raster scan order.…”

Spatially and Spectrally Concatenated Neural Networks for Efficient Lossless Compression of Hyperspectral Imagery

“…Information theoretic analysis can provide an upper bound on the amount of compression achievable based on the specific context. The analysis employs the concept of conditional entropy, as a measure of information gain, based on a simple model of prediction process [ 29 ].…”