A METHOD OF REORDERING LOSSLESS COMPRESSION OF HYPERSPECTRAL IMAGES
Abstract. An improved lossless compression method with adaptive band reordering and minimum mean square error prediction was proposed to address the problems of huge data volume of remote sensing images, great pressure on transmission and storage and a low compression ratio. This method may determine the optimal band ordering adaptively, and make full use of the ordering correlation to eliminate the image redundancy according to the minimum mean square error criterion. First, it adaptively grouped hyperspectral image bands, and used the minimum spanning tree algorithm for band ordering within each group to enhance the inter-spectral correlation of adjacent bands. Later, it selected the contexts for inter- and intra-spectral prediction adaptively for the bands within the group to remove the redundancy of hyperspectral images. Finally, it conducted binary arithmetic coding of the predicted residuals to remove the statistical redundancy, and complete the lossless compression of hyperspectral images. The test results based on the hyperspectral images of ZY1-02D show that the method in this paper effectively utilizes the intra- and inter-spectral correlations, improves the prediction performance, and outperforms the commonly used compression methods.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A METHOD OF REORDERING LOSSLESS COMPRESSION OF HYPERSPECTRAL IMAGES ; volume:X-1/W1-2023 ; year:2023 ; pages:821-826 ; extent:6
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-1/W1-2023 (2023), 821-826 (gesamt 6)
- Klassifikation
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Elektrotechnik, Elektronik
- Urheber
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Gao, X.
Wang, L.
Li, T.
Xie, J.
- DOI
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10.5194/isprs-annals-X-1-W1-2023-821-2023
- URN
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urn:nbn:de:101:1-2023120703145916202540
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 05:33 UTC
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Gao, X.
- Wang, L.
- Li, T.
- Xie, J.
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