Public policies and private initiatives share the will to explore outer space and to monitor the Earth from space sensors. Recent years have seen an increased number of space missions, while the sensors on board aircrafts or spacecrafts have also significantly improved their acquisition capabilities. Given this huge volume of remote sensing data and the detailed characteristics of the acquired images, a data compression process is in order to allow as large a transmission rate as possible.In this paper we provide an overview of several standards for remote sensing data compression, notably of those recently approved by the Consultative Committee for Space Data Systems, although the use of other ISO/IEC image coding standards is also dealt with. Discussion embraces both mono band and multi band compression, and lossless, lossy and near-lossless compression.Illustrative results are reported for a set of AVIRIS and Hyperion images, indicating that exploiting the spectral correlation -either in prediction-based or in transform-based schemes-is paramount to achieve improved coding performance.Index Terms-image compression, multispectral and hyperspectral images, CCSDS, prediction, transform coding, DPCM, rate control.Post-print of: "A tutorial on image compression for optical space imaging systems" / I.
The Karhunen-Loêve transform (KLT) is widely used in hyperspectral image compression because of its high spectral decorrelation properties. However, its use entails a very high computational cost. To overcome this computational cost and to increase its scalability, in this paper, we introduce a multilevel clustering approach for the KLT. As the set of different multilevel clustering structures is very large, a two-stage process is used to carefully pick the best members for each specific situation. First, several candidate structures are generated through local search and eigenthresholding methods, and then, candidates are further screened to select the best clustering configuration. Two multilevel clustering combinations are proposed for hyperspectral image compression: one with the coding performance of the KLT but with much lower computational requirements and increased scalability and another one that outperforms a lossy wavelet transform, as spectral decorrelator, in quality, cost, and scalability. Extensive experimental validation is performed, with images from both the AVIRIS and Hyperion sets, and with JPEG2000, 3D-TCE, and CCSDS-Image Data Compression recommendation as image coders. Experiments also include classification-based results produced by k-means clustering and Reed-Xiaoli anomaly detection.
A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed Regression Wavelet Analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion and IASI, suggest that RWA outperforms not only Principal Component Analysis (PCA) and Wavelets, but also the best and most recent coding standard in remote sensing, CCSDS-123.
Index TermsTransform coding via regression, wavelet-based transform coding, remote sensing data compression, redundancy in hyperspectral images.
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