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008			210220s2021 si | s |||| 0|eng d
020			_a9789813344204
024	7		_a10.1007/978-981-33-4420-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637 _b2021 EB
100	1		_aTao, Linmi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9679106
245	1	0	_aDeep Learning for Hyperspectral Image Analysis and Classification _cby Linmi Tao, Atif Mughees
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XII, 207 páginas) _b121 ilustraciones, 106 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aEngineering Applications of Computational Methods _x2662-3366 _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	3		_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 the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
988			_aSpringer_Computer_2021
650		7	_2embne _9669495 _aProceso de imágenes
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aMughees, Atif _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9679107
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9789813344198
776	0	8	_iPrinted edition: _z9789813344211
776	0	8	_iPrinted edition: _z9789813344228
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-33-4420-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2021 _dz _eIG _zSI