000 | 03816nam a22006015i 4500 | ||
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001 | 978-981-33-4420-4 | ||
003 | DE-He213 | ||
005 | 20240423125437.0 | ||
007 | cr nn 008mamaa | ||
008 | 210220s2021 si | s |||| 0|eng d | ||
020 |
_a9789813344204 _9978-981-33-4420-4 |
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024 | 7 |
_a10.1007/978-981-33-4420-4 _2doi |
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050 | 4 | _aQ325.5-.7 | |
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_aUYQM _2bicssc |
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_aUYQM _2thema |
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082 | 0 | 4 |
_a006.31 _223 |
100 | 1 |
_aTao, Linmi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aDeep Learning for Hyperspectral Image Analysis and Classification _h[electronic resource] / _cby Linmi Tao, Atif Mughees. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021. |
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300 |
_aXII, 207 p. 121 illus., 106 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aEngineering Applications of Computational Methods, _x2662-3374 ; _v5 |
|
505 | 0 | _aIntroduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion. | |
520 | _aThis book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends. | ||
650 | 0 | _aMachine learning. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aSignal processing. | |
650 | 1 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
650 | 2 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aSignal, Speech and Image Processing. |
700 | 1 |
_aMughees, Atif. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789813344198 |
776 | 0 | 8 |
_iPrinted edition: _z9789813344211 |
776 | 0 | 8 |
_iPrinted edition: _z9789813344228 |
830 | 0 |
_aEngineering Applications of Computational Methods, _x2662-3374 ; _v5 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-33-4420-4 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c177995 _d177995 |