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.

A Tutorial on Image Compression for Optical Space Imaging Systems / Ian, Blanes; Magli, Enrico; Joan Serra, Sagristà. - In: IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. - ISSN 2168-6831. - STAMPA. - 2014:9(2014), pp. 8-26. [10.1109/MGRS.2014.2352465]

A Tutorial on Image Compression for Optical Space Imaging Systems

MAGLI, ENRICO;
2014

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2592608
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