國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Hyperspectral image analysisadvances...

Chanussot, Jocelyn.

Hyperspectral image analysisadvances in machine learning and signal processing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hyperspectral image analysisedited by Saurabh Prasad, Jocelyn Chanussot.
其他題名:	advances in machine learning and signal processing /
其他作者:	Prasad, Saurabh.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	vi, 466 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Image processing.
電子資源:	https://doi.org/10.1007/978-3-030-38617-7
ISBN:	9783030386177$q(electronic bk.)

Hyperspectral image analysisadvances in machine learning and signal processing /
Hyperspectral image analysisadvances in machine learning and signal processing /[electronic resource] :edited by Saurabh Prasad, Jocelyn Chanussot. - Cham :Springer International Publishing :2020. - vi, 466 p. :ill., digital ;24 cm. - Advances in computer vision and pattern recognition,2191-6586. - Advances in computer vision and pattern recognition..

1. Introduction -- 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation -- 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms -- 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine -- 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios -- 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. Dr. Saurabh Prasad is an Associate Professor at the Department of Electrical and Computer Engineering at the University of Houston, TX, USA. Dr. Jocelyn Chanussot is a Professor in the Signal and Images Department at Grenoble Institute of Technology, France.

ISBN: 9783030386177$q(electronic bk.)

Standard No.: 10.1007/978-3-030-38617-7doiSubjects--Topical Terms:

182627
Image processing.

LC Class. No.: TA1637

Dewey Class. No.: 006.37

Hyperspectral image analysisadvances in machine learning and signal processing /
LDR:03200nmm a2200349 a 4500 001 571753
003 DE-He213
005 20200427222548.0
006 m d
007 cr nn 008maaau
008 200922s2020 sz s 0 eng d
020 $a 9783030386177$q(electronic bk.)
020 $a 9783030386160$q(paper)
024 7 $a 10.1007/978-3-030-38617-7 $2 doi
035 $a 978-3-030-38617-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1637
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1637 $b .H998 2020
245 0 0 $a Hyperspectral image analysis $h [electronic resource] : $b advances in machine learning and signal processing / $c edited by Saurabh Prasad, Jocelyn Chanussot.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a vi, 466 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in computer vision and pattern recognition, $x 2191-6586
505 0 $a 1. Introduction -- 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation -- 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms -- 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine -- 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios -- 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.
520 $a This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. Dr. Saurabh Prasad is an Associate Professor at the Department of Electrical and Computer Engineering at the University of Houston, TX, USA. Dr. Jocelyn Chanussot is a Professor in the Signal and Images Department at Grenoble Institute of Technology, France.
650 0 $a Image processing. $3 182627
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Remote Sensing/Photogrammetry. $3 274522
650 2 4 $a Signal, Image and Speech Processing. $3 273768
700 1 $a Prasad, Saurabh. $3 511665
700 1 $a Chanussot, Jocelyn. $3 511667
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Advances in computer vision and pattern recognition. $3 559645
856 4 0 $u https://doi.org/10.1007/978-3-030-38617-7
950 $a Computer Science (Springer-11645)