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.
Highlights
Hyperspectral imaging (HSI) technologies have been widely used in many applications of remote sensing (RS) owing to the high spatial and spectral resolutions of hyperspectral images [1]
While the actual amount of compression achieved depends on the choice of specific compression algorithms [31], information theoretic analysis can provide us an upper bound on the amount of compression achievable
We can observe that as either p or q increases, the general trend is that the conditional entropy decreases; as p or q further increases, the reduction of entropy becomes smaller than the case of either p or q going from 0 to 1. This means that including a few previous bands either spectrally or temporally in the context can be very useful to improving the performance of the prediction-based compression algorithms, but the return of adding more bands from distant past will diminish as the correlations get weaker, let alone the increased computational cost associated with involving excessive number of bands for prediction
Summary
Hyperspectral imaging (HSI) technologies have been widely used in many applications of remote sensing (RS) owing to the high spatial and spectral resolutions of hyperspectral images [1]. Aiazzi et al proposed a predictive method leveraging crisp or fuzzy clustering to produce state-of-the-art results Later, authors in both [20,21] again utilized the K-means clustering algorithm to improve the compression efficiency. A low-complexity method called the “Fast Lossless” (FL) method, proposed by the NASA Jet Propulsion Lab (JPL) in [22], was selected as the core predictor in the Consultative Committee for Space Data Systems (CCSDS) new standard for multispectral and hyperspectral data compression [23], to provide efficient compression on 3D HSI data. We propose a low-complexity linear prediction algorithm, which extends the well-known FL method into a 4D version to achieve higher data compression, by better adapting to the underlying statistics of multitemporal HSI data.
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