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020 _a9789813344204
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024 7 _a10.1007/978-981-33-4420-4
_2doi
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_2bicssc
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082 0 4 _a006.31
_223
100 1 _aTao, Linmi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXII, 207 p. 121 illus., 106 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
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