Classification Of Hyperspectral Remote Sensing Images
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Spectral Spatial Classification Of Hyperspectral Remote Sensing Images
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Author : Jon Atli Benediktsson
language : en
Publisher: Artech House
Release Date : 2015-09-01
Spectral Spatial Classification Of Hyperspectral Remote Sensing Images written by Jon Atli Benediktsson and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-01 with Technology & Engineering categories.
This comprehensive new resource brings you up to date on recent developments in the classification of hyperspectral images using both spectral and spatial information, including advanced statistical approaches and methods. The inclusion of spatial information to traditional approaches for hyperspectral classification has been one of the most active and relevant innovative lines of research in remote sensing during recent years. This book gives you insight into several important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. This book also shows you how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide.
Hyperspectral Remote Sensing
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Author : Michael Theodore Eismann
language : en
Publisher: SPIE-International Society for Optical Engineering
Release Date : 2012
Hyperspectral Remote Sensing written by Michael Theodore Eismann and has been published by SPIE-International Society for Optical Engineering this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Image processing categories.
Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment that captures its multidisciplinary nature. The content is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available in this technology area and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.
Classification Of Hyperspectral Remote Sensing Images
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Author :
language : en
Publisher:
Release Date : 2018-05
Classification Of Hyperspectral Remote Sensing Images written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05 with categories.
Recent advances in hyperspectral remote sensor technology allow the simultaneous acquisition of hundreds of spectral wavelengths for each image pixel. Hyperspectral imaging systems can acquire numerous contiguous spectral bands throughout the electromagnetic spectrum. Therefore, hyperspectral imaging techniques are widely used for many applications, including environmental monitoring, mineralogy, astronomy, surveillance and defense. Nevertheless, the high dimensionality of the pixels, undesirable noise, high spectral redundancy and spectral and spatial variabilities, in conjunction with limited ground truth data, present challenges for the analysis of hyperspectral imagery. The classification technology is currently the predominate method for analyzing hyperspectral images and has received much attention. Over the past decades, numerous pixel-wise classification methods, which only use spectral information, have been proposed to classify remote sensing images. Recent advances in spectral-spatial classification of hyperspectral images are presented in this book. Several techniques are investigated for combining both spatial and spectral information. The book highlights the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validates the proposed methods. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images presents insight into numerous important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. The book also demonstrates the reader how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide.
Deep Learning For Hyperspectral Image Analysis And Classification
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Author : Linmi Tao
language : en
Publisher: Springer Nature
Release Date : 2021-02-20
Deep Learning For Hyperspectral Image Analysis And Classification written by Linmi Tao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Computers categories.
This 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.
Hyperspectral Imaging Remote Sensing
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Author : Dimitris G. Manolakis
language : en
Publisher: Cambridge University Press
Release Date : 2016-10-20
Hyperspectral Imaging Remote Sensing written by Dimitris G. Manolakis and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-20 with Computers categories.
Understand the seminal principles, current techniques, and tools of imaging spectroscopy with this self-contained introductory guide.
2021 Ieee Conference Of Russian Young Researchers In Electrical And Electronic Engineering Elconrus
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-01-26
2021 Ieee Conference Of Russian Young Researchers In Electrical And Electronic Engineering Elconrus written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-26 with categories.
The conference will cover a broad area of electrical and electronic engineering, computer science and engineering, biomedical engineering, industrial management It is targeted on results of research carried out by young researchers (Master and PhD students, engineers)
Hyperspectral Remote Sensing
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Author : Marcus Borengasser
language : en
Publisher: CRC Press
Release Date : 2007-12-13
Hyperspectral Remote Sensing written by Marcus Borengasser and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-13 with Technology & Engineering categories.
Land management issues, such as mapping tree species, recognizing invasive plants, and identifying key geologic features, require an understanding of complex technical issues before the best decisions can be made. Hyperspectral remote sensing is one the technologies that can help with reliable detection and identification. Presenting the fundamenta
Knowledge Based Intelligent Information And Engineering Systems
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Author : Ignac Lovrek
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-08-18
Knowledge Based Intelligent Information And Engineering Systems written by Ignac Lovrek and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-18 with Business & Economics categories.
Annotation The three volume set LNAI 5177, LNAI 5178, and LNAI 5179, constitutes the refereed proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, held in Zagreb, Croatia, in September 2008. The 316 revised papers presented were carefully reviewed and selected. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the first volume are artificial neural networks and connectionists systems; fuzzy and neuro-fuzzy systems; evolutionary computation; machine learning and classical AI; agent systems; knowledge based and expert systems; intelligent vision and image processing; knowledge management, ontologies, and data mining; Web intelligence, text and multimedia mining and retrieval; and intelligent robotics and control.
Hyperspectral Image Analysis
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Author : Saurabh Prasad
language : en
Publisher: Springer
Release Date : 2020-06-09
Hyperspectral Image Analysis written by Saurabh Prasad and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-09 with Computers categories.
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.