In this paper we propose a lossless and lossy onboard compression algorithm for multispectral and hyperspectral images, based on the recent CCSDS-123.0-B-2 standard, which takes advantage of cloud screening in order to perform data volume reduction, by avoiding to transmit pixels that are covered by clouds. In particular, we develop methods addressing two problems: i) how to signal the cloud mask in the compressed file, and ii) how to handle cloudy pixels in order to maximize the amount of compression. Experimental results on a set of LANDSAT 8 ETM+ and AVIRIS images show a significant data volume reduction with respect to the plain use of the CCSDS-123.0-B-2 standard.
Onboard Data Reduction for Multispectral and Hyperspectral Images via Cloud Screening / Cilia, Martina; Prette, Nicola; Magli, Enrico; Sang, Bernhard; Pieraccini, Stefano. - ELETTRONICO. - (2020), pp. 6230-6233. (Intervento presentato al convegno IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium tenutosi a Hawaii, USA nel 26 Sept.-2 Oct. 2020) [10.1109/IGARSS39084.2020.9323641].
Onboard Data Reduction for Multispectral and Hyperspectral Images via Cloud Screening
Martina Cilia;Nicola Prette;Enrico Magli;
2020
Abstract
In this paper we propose a lossless and lossy onboard compression algorithm for multispectral and hyperspectral images, based on the recent CCSDS-123.0-B-2 standard, which takes advantage of cloud screening in order to perform data volume reduction, by avoiding to transmit pixels that are covered by clouds. In particular, we develop methods addressing two problems: i) how to signal the cloud mask in the compressed file, and ii) how to handle cloudy pixels in order to maximize the amount of compression. Experimental results on a set of LANDSAT 8 ETM+ and AVIRIS images show a significant data volume reduction with respect to the plain use of the CCSDS-123.0-B-2 standard.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2912413