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Optical Remote Sensing - Advances in Signal Processing and Exploitation Techniques (Hardcover, 2011 ed.): Saurabh Prasad, Lori... Optical Remote Sensing - Advances in Signal Processing and Exploitation Techniques (Hardcover, 2011 ed.)
Saurabh Prasad, Lori M Bruce, Jocelyn Chanussot
R4,275 Discovery Miles 42 750 Ships in 10 - 15 working days

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data. Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.

Hyperspectral Image Analysis - Advances in Machine Learning and Signal Processing (Hardcover, 1st ed. 2020): Saurabh Prasad,... Hyperspectral Image Analysis - Advances in Machine Learning and Signal Processing (Hardcover, 1st ed. 2020)
Saurabh Prasad, Jocelyn Chanussot
R4,067 Discovery Miles 40 670 Ships in 12 - 17 working days

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.

Optical Remote Sensing - Advances in Signal Processing and Exploitation Techniques (Paperback, 2011 ed.): Saurabh Prasad, Lori... Optical Remote Sensing - Advances in Signal Processing and Exploitation Techniques (Paperback, 2011 ed.)
Saurabh Prasad, Lori M Bruce, Jocelyn Chanussot
R4,245 Discovery Miles 42 450 Ships in 10 - 15 working days

Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data. Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.

Hyperspectral Image Analysis - Advances in Machine Learning and Signal Processing (Paperback, 1st ed. 2020): Saurabh Prasad,... Hyperspectral Image Analysis - Advances in Machine Learning and Signal Processing (Paperback, 1st ed. 2020)
Saurabh Prasad, Jocelyn Chanussot
R4,280 Discovery Miles 42 800 Ships in 10 - 15 working days

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.

Mathematical Morphology and Its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015,... Mathematical Morphology and Its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Reykjavik, Iceland, May 27-29, 2015. Proceedings (Paperback, 2015 ed.)
Jon Atli Benediktsson, Jocelyn Chanussot, Laurent Najman, Hugues Talbot
R1,607 Discovery Miles 16 070 Ships in 10 - 15 working days

This book contains the thoroughly refereed proceedings of the 12th International Symposium on Mathematical Morphology, ISMM 2015 held in Reykjavik, Iceland, in May 2015. The 62 revised full papers were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on evaluations and applications; hierarchies; color, multivalued and orientation fields; optimization, differential calculus and probabilities; topology and discrete geometry; and algorithms and implementation.

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