Difference between revisions of "Publications/tochon.17.tgrs"
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{{Publication |
{{Publication |
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| published = true |
| published = true |
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− | | date = 2017- |
+ | | date = 2017-04-20 |
| authors = Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi |
| authors = Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi |
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| title = Object tracking by hierarchical decomposition of hyperspectral video sequences: Application to chemical gas plume tracking |
| title = Object tracking by hierarchical decomposition of hyperspectral video sequences: Application to chemical gas plume tracking |
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| lrdeprojects = Olena |
| lrdeprojects = Olena |
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| lrdekeywords = Image |
| lrdekeywords = Image |
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+ | | lrdepaper = http://www.lrde.epita.fr/dload/papers/tochon.17.tgrs.pdf |
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− | | lrdenewsdate = 2017- |
+ | | lrdenewsdate = 2017-04-20 |
| type = article |
| type = article |
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| id = tochon.17.tgrs |
| id = tochon.17.tgrs |
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+ | | identifier = doi:10.1109/TGRS.2017.2694159 |
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| bibtex = |
| bibtex = |
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@Article<nowiki>{</nowiki> tochon.17.tgrs, |
@Article<nowiki>{</nowiki> tochon.17.tgrs, |
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gas plume tracking<nowiki>}</nowiki>, |
gas plume tracking<nowiki>}</nowiki>, |
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journal = <nowiki>{</nowiki>IEEE Transactions on Geoscience and Remote Sensing<nowiki>}</nowiki>, |
journal = <nowiki>{</nowiki>IEEE Transactions on Geoscience and Remote Sensing<nowiki>}</nowiki>, |
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− | volume = |
+ | volume = 55, |
− | number = |
+ | number = 8, |
pages = <nowiki>{</nowiki>4567--4585<nowiki>}</nowiki>, |
pages = <nowiki>{</nowiki>4567--4585<nowiki>}</nowiki>, |
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month = aug, |
month = aug, |
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− | year = |
+ | year = 2017, |
abstract = <nowiki>{</nowiki>It is now possible to collect hyperspectral video |
abstract = <nowiki>{</nowiki>It is now possible to collect hyperspectral video |
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sequences at a near real-time frame rate. The wealth of |
sequences at a near real-time frame rate. The wealth of |
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compare its performances with state-of-the-art methods, for |
compare its performances with state-of-the-art methods, for |
||
two real hyperspectral video sequences, and show that the |
two real hyperspectral video sequences, and show that the |
||
− | proposed approach performs at least equally well.<nowiki>}</nowiki> |
+ | proposed approach performs at least equally well.<nowiki>}</nowiki>, |
+ | doi = <nowiki>{</nowiki>10.1109/TGRS.2017.2694159<nowiki>}</nowiki> |
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<nowiki>}</nowiki> |
<nowiki>}</nowiki> |
||
Latest revision as of 17:01, 27 May 2021
- Authors
- Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea Bertozzi
- Journal
- IEEE Transactions on Geoscience and Remote Sensing
- Type
- article
- Projects
- Olena
- Keywords
- Image
- Date
- 2017-04-20
Abstract
It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this article, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection processconducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.
Documents
Bibtex (lrde.bib)
@Article{ tochon.17.tgrs, author = {Guillaume Tochon and Jocelyn Chanussot and Mauro {Dalla Mura} and Andrea Bertozzi}, title = {Object tracking by hierarchical decomposition of hyperspectral video sequences: {A}pplication to chemical gas plume tracking}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = 55, number = 8, pages = {4567--4585}, month = aug, year = 2017, abstract = {It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this article, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.}, doi = {10.1109/TGRS.2017.2694159} }