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001434407 001__ 1434407
001434407 003__ OCoLC
001434407 005__ 20230309003727.0
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001434407 008__ 210227s2021\\\\si\\\\\\ob\\\\000\0\eng\d
001434407 019__ $$a1239801395$$a1244119162
001434407 020__ $$a9789813344204$$q(electronic book)
001434407 020__ $$a9813344202$$q(electronic book)
001434407 020__ $$z9813344199
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001434407 0247_ $$a10.1007/978-981-33-4420-4$$2doi
001434407 035__ $$aSP(OCoLC)1239987066
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001434407 049__ $$aISEA
001434407 050_4 $$aTA1637
001434407 08204 $$a006.4/2$$223
001434407 1001_ $$aTao, Linmi,$$eauthor.
001434407 24510 $$aDeep learning for hyperspectral image analysis and classification /$$cLinmi Tao, Atif Mighees.
001434407 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001434407 300__ $$a1 online resource
001434407 336__ $$atext$$btxt$$2rdacontent
001434407 337__ $$acomputer$$bc$$2rdamedia
001434407 338__ $$aonline resource$$bcr$$2rdacarrier
001434407 347__ $$atext file
001434407 347__ $$bPDF
001434407 4901_ $$aEngineering applications of computational methods ;$$vvolume 5
001434407 504__ $$aIncludes bibliographical references.
001434407 5050_ $$aIntroduction -- Hyperspectral image and classification approaches -- Unsupervised hyperspectral image noise reduction and band categorization -- Hyperspectral image spatial feature extraction via segmentation -- Integrating spectral-spatial information for deep learnign based HSI classification -- Multi-deep net based hyperspectral image classification -- Sparse-based hyperspectral data classification -- Challenges and future prospects.
001434407 506__ $$aAccess limited to authorized users.
001434407 520__ $$aThis book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
001434407 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 17, 2021).
001434407 650_0 $$aHyperspectral imaging$$xData processing.
001434407 650_0 $$aMachine learning.
001434407 650_0 $$aImage processing$$xDigital techniques.
001434407 650_0 $$aAutomatic classification.
001434407 650_6 $$aApprentissage automatique.
001434407 650_6 $$aTraitement d'images$$xTechniques numériques.
001434407 650_6 $$aClassification automatique.
001434407 655_0 $$aElectronic books.
001434407 7001_ $$aMughees, Atif,$$eauthor.
001434407 77608 $$iPrint version:$$z9789813344198
001434407 830_0 $$aEngineering applications of computational methods ;$$v5.
001434407 852__ $$bebk
001434407 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4420-4$$zOnline Access$$91397441.1
001434407 909CO $$ooai:library.usi.edu:1434407$$pGLOBAL_SET
001434407 980__ $$aBIB
001434407 980__ $$aEBOOK
001434407 982__ $$aEbook
001434407 983__ $$aOnline
001434407 994__ $$a92$$bISE