소장자료
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| 020 | ▼a9783030386177▼9978-3-030-38617-7▲ | ||
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| 245 | 1 | 0 | ▼aHyperspectral Image Analysis▼h[electronic resource] :▼bAdvances in Machine Learning and Signal Processing /▼cedited by Saurabh Prasad, Jocelyn Chanussot.▲ |
| 250 | ▼a1st ed. 2020.▲ | ||
| 264 | 1 | ▼aCham :▼bSpringer International Publishing :▼bImprint: Springer,▼c2020.▲ | |
| 300 | ▼aVI, 466 p. 170 illus., 144 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 | ▼aAdvances in Computer Vision and Pattern Recognition,▼x2191-6586▲ | |
| 505 | 0 | ▼a1. 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 | ▼aThis 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 | ▼aOptical data processing.▲ | |
| 650 | 0 | ▼aArtificial intelligence.▲ | |
| 650 | 0 | ▼aRemote sensing.▲ | |
| 650 | 0 | ▼aSignal processing.▲ | |
| 650 | 0 | ▼aImage processing.▲ | |
| 650 | 0 | ▼aSpeech processing systems.▲ | |
| 650 | 1 | 4 | ▼aImage Processing and Computer Vision.▼0https://scigraph.springernature.com/ontologies/product-market-codes/I22021▲ |
| 650 | 2 | 4 | ▼aArtificial Intelligence.▼0https://scigraph.springernature.com/ontologies/product-market-codes/I21000▲ |
| 650 | 2 | 4 | ▼aRemote Sensing/Photogrammetry.▼0https://scigraph.springernature.com/ontologies/product-market-codes/J13010▲ |
| 650 | 2 | 4 | ▼aSignal, Image and Speech Processing.▼0https://scigraph.springernature.com/ontologies/product-market-codes/T24051▲ |
| 700 | 1 | ▼aPrasad, Saurabh.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aChanussot, Jocelyn.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 710 | 2 | ▼aSpringerLink (Online service)▲ | |
| 773 | 0 | ▼tSpringer Nature eBook▲ | |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783030386160▲ |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783030386184▲ |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783030386191▲ |
| 830 | 0 | ▼aAdvances in Computer Vision and Pattern Recognition,▼x2191-6586▲ | |
| 856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-3-030-38617-7▲ |
Hyperspectral Image Analysis : Advances in Machine Learning and Signal Processing
자료유형
국외eBook
서명/책임사항
Hyperspectral Image Analysis [electronic resource] : Advances in Machine Learning and Signal Processing / edited by Saurabh Prasad, Jocelyn Chanussot.
판사항
1st ed. 2020.
형태사항
VI, 466 p. 170 illus., 144 illus. in color. online resource.
총서사항
내용주기
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.
요약주기
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.
주제
ISBN
9783030386177
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