Hyperspectral image, video compression using sparse tucker tensor decomposition
Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume...
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| Published in: | IET image processing Vol. 15; no. 4; pp. 964 - 973 |
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| Format: | Journal Article |
| Language: | English |
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01.03.2021
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| ISSN: | 1751-9659, 1751-9667 |
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| Abstract | Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume, efficient methods are required to compress the data. This paper proposes a multi‐way approach for compression of the hyperspectral image or video sequence. In this approach, a differential representation of the data is first obtained. In the case of hyperspectral images, the difference between consecutive bands is obtained and in case of videos, the difference between consecutive frames is computed. In the next step, a sparse Tucker tensor decomposition is performed and the sparse core tensor obtained. Finally, the core tensor and the corresponding factor matrices are truncated and the data encoded to obtain the compressed version for transmission. The compression method utilises the multi‐way structure of the data and hence can be extended for hyperspectral videos. Experimental results on several real data imply that the proposed compression approach obtains better efficiency in terms of compression ratio, signal to noise ratio. |
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| AbstractList | Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume, efficient methods are required to compress the data. This paper proposes a multi‐way approach for compression of the hyperspectral image or video sequence. In this approach, a differential representation of the data is first obtained. In the case of hyperspectral images, the difference between consecutive bands is obtained and in case of videos, the difference between consecutive frames is computed. In the next step, a sparse Tucker tensor decomposition is performed and the sparse core tensor obtained. Finally, the core tensor and the corresponding factor matrices are truncated and the data encoded to obtain the compressed version for transmission. The compression method utilises the multi‐way structure of the data and hence can be extended for hyperspectral videos. Experimental results on several real data imply that the proposed compression approach obtains better efficiency in terms of compression ratio, signal to noise ratio. Abstract Hyperspectral image and videos provide rich spectral information content, which facilitates accurate classification, unmixing, temporal change detection, and so on. However, with the rapid improvements in technology, the data size has increased many folds. To properly handle the enormous data volume, efficient methods are required to compress the data. This paper proposes a multi‐way approach for compression of the hyperspectral image or video sequence. In this approach, a differential representation of the data is first obtained. In the case of hyperspectral images, the difference between consecutive bands is obtained and in case of videos, the difference between consecutive frames is computed. In the next step, a sparse Tucker tensor decomposition is performed and the sparse core tensor obtained. Finally, the core tensor and the corresponding factor matrices are truncated and the data encoded to obtain the compressed version for transmission. The compression method utilises the multi‐way structure of the data and hence can be extended for hyperspectral videos. Experimental results on several real data imply that the proposed compression approach obtains better efficiency in terms of compression ratio, signal to noise ratio. |
| Author | Das, Samiran |
| Author_xml | – sequence: 1 givenname: Samiran orcidid: 0000-0003-4895-1270 surname: Das fullname: Das, Samiran email: itzsamirandas@gmail.com organization: Indian Institute of Technology Kharagpur |
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| Cites_doi | 10.1109/MSP.2013.2279731 10.1109/TGRS.2016.2574757 10.1109/TGRS.2019.2927434 10.1007/BF02294556 10.1109/JSTARS.2019.2917088 10.1007/978-3-319-13254-9_72 10.3390/rs10060907 10.1109/JSTARS.2017.2666264 10.1016/S0167-9473(99)00017-1 10.1016/j.ins.2019.02.008 10.1109/SCIS-ISIS.2014.7044685 10.1109/TGRS.2015.2478379 10.1109/JSTARS.2011.2173906 10.1109/LGRS.2004.824762 10.1007/s11042-017-4724-8 10.1109/ICIP.2003.1246661 10.1109/IPTA.2010.5586739 10.1109/TGRS.2010.2098413 10.1016/j.neucom.2014.06.052 10.1109/LGRS.2018.2888580 10.1109/LSP.2005.862604 10.1109/JSTARS.2014.2320754 10.1109/TGRS.2004.831865 10.1109/36.823937 10.1109/TIT.2009.2027527 10.1109/LGRS.2005.859942 10.1049/el.2010.1788 10.1109/IGARSS.2005.1526121 10.1109/TSP.2006.881199 10.1109/MSP.2013.2297439 10.1364/OE.20.010658 10.1109/LGRS.2006.888109 10.1137/07070111X 10.1109/TCYB.2016.2605044 10.1109/LGRS.2016.2644726 10.1109/TSP.2016.2620965 10.1109/TSP.2017.2690524 |
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| Title | Hyperspectral image, video compression using sparse tucker tensor decomposition |
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