Compression and multifunctionality support of multispectral satellite data

Alin Alecu; Adrian Munteanu; Peter Schelkens; Jan P.H. Cornelis; Steven Dewitte

doi:10.1117/12.413912

19 January 2001 Compression and multifunctionality support of multispectral satellite data

Alin Alecu, Adrian Munteanu, Peter Schelkens, Jan P.H. Cornelis, Steven Dewitte

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413912
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

The paper deals with the compression of multi-spectral satellite image data (high resolution data consisting of radiances and top-of—the-atmosphere TOA fluxes) investigated within the framework of the EUMETSAT project of SAP (Satellite Application Facility) on Climate Monitoring. Full multifunctionality support (quality scalability, resolution scalability, region-of-interest access) is asked for and image calibration characteristics (luminance, radiance) must be preserved within certain limits for lossy image compression, together with an excellent image quality. We analyze state-of-the-art coding techniques with respect to these requirements. Our objective is to answer two questions, namely the capability of existing state-of-the-art compression techniques to comply with our image calibration characteristics requirements and the support they offer regarding multifunctionality (in terms of quality scalability, resolution scalability and region of interest access). We propose basic modifications of these techniques so as to meet our multifunctionality requirements. We conclude from the experimental assessment of the analyzed techniques that none of the top coding algorithms available to date fully satisfy our imposed rate-distortion constraints and propose a path for future research in this field.

Citation Download Citation

Alin Alecu, Adrian Munteanu, Peter Schelkens, Jan P.H. Cornelis, and Steven Dewitte "Compression and multifunctionality support of multispectral satellite data", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413912

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