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| 008 |
210512 ||| eng |
| 020 |
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|a 9781789851090
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| 020 |
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|a 9781838804626
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| 020 |
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|a intechopen.78179
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| 020 |
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|a 9781789851106
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| 100 |
1 |
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|a Chen, Jie
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| 245 |
0 |
0 |
|a Processing and Analysis of Hyperspectral Data
|h Elektronische Ressource
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| 260 |
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|b IntechOpen
|c 2020
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| 300 |
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|a 1 electronic resource (136 p.)
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| 653 |
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|a Geophysics / bicssc
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| 653 |
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|a Geophysics
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| 700 |
1 |
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|a Song, Yingying
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| 700 |
1 |
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|a Li, Hengchao
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| 700 |
1 |
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|a Chen, Jie
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/3.0/
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| 028 |
5 |
0 |
|a 10.5772/intechopen.78179
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/67224
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://mts.intechopen.com/storage/books/8223/authors_book/authors_book.pdf
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 530
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| 520 |
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|a Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods.
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