Applications of Deep Learning to Radar Polarimetry: A Physics First Approach to Machine Learning in Radar Earth Observation Applications - Couverture souple

De, Shaunak

9786139815999: Applications of Deep Learning to Radar Polarimetry: A Physics First Approach to Machine Learning in Radar Earth Observation Applications

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ISBN 10 : 6139815991 ISBN 13 : 9786139815999

Editeur : LAP LAMBERT Academic Publishing, 2018

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Radar remote sensing has made significant technological and scientific advances in the past few years. Sensors and constellations are able to acquire high resolution, polarimetric, wide swath data with high temporal repetivity. This has lead to an exponential increase in the volume of data available. With more temporally dense constellations planned in the near future, it is imperative that automated techniques based on machine learning algorithms be developed that are able to take advantage of all the acquired data and convert latent information to actionable knowledge. However, the use of indiscriminate machine learning techniques can be problematic since there is no guarantee that the learned model makes sense from a physical standpoint. Advanced neural network techniques, collectively called 'deep leaning' algorithms have demonstrated the ability to self-learn features from a data-volume, greatly reducing the need for time-consuming feature tuning. In this book, novel deep learning algorithms and architectures are detailed for various earth observation applications using fully polarimetric SAR data based, and constrained by the principles of scattering physics.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2018
Langue: anglais
ISBN 10: 6139815991
ISBN 13: 9786139815999
Reliure: Broch�
Nombre de pages: 212
Coordonn�es du fabricant: non disponible
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Applications of Deep Learning to Radar Polarimetry

Shaunak de

Edit� par LAP LAMBERT Academic Publishing Jul 2018, 2018

ISBN 10 : 6139815991 ISBN 13 : 9786139815999

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Radar remote sensing has made significant technological and scientific advances in the past few years. Sensors and constellations are able to acquire high resolution, polarimetric, wide swath data with high temporal repetivity. This has lead to an exponential increase in the volume of data available. With more temporally dense constellations planned in the near future, it is imperative that automated techniques based on machine learning algorithms be developed that are able to take advantage of all the acquired data and convert latent information to actionable knowledge. However, the use of indiscriminate machine learning techniques can be problematic since there is no guarantee that the learned model makes sense from a physical standpoint. Advanced neural network techniques, collectively called 'deep leaning' algorithms have demonstrated the ability to self-learn features from a data-volume, greatly reducing the need for time-consuming feature tuning. In this book, novel deep learning algorithms and architectures are detailed for various earth observation applications using fully polarimetric SAR data based, and constrained by the principles of scattering physics. 212 pp. Englisch. N� de r�f. du vendeur 9786139815999

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Applications of Deep Learning to Radar Polarimetry

Shaunak De

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: De ShaunakDr Shaunak De received the B.Eng. in electronics from the University of Mumbai in 2012 (gold medalist) and the PhD from Indian Institute of Technology Bombay in 2018. He s worked extensively in the field of remote sensing, . N� de r�f. du vendeur 385872205

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Applications of Deep Learning to Radar Polarimetry | A Physics First Approach to Machine Learning in Radar Earth Observation Applications

Shaunak de

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ISBN 10 : 6139815991 ISBN 13 : 9786139815999

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Applications of Deep Learning to Radar Polarimetry

Shaunak de

Edit� par LAP LAMBERT Academic Publishing Jul 2018, 2018

ISBN 10 : 6139815991 ISBN 13 : 9786139815999

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Radar remote sensing has made significant technological and scientific advances in the past few years. Sensors and constellations are able to acquire high resolution, polarimetric, wide swath data with high temporal repetivity. This has lead to an exponential increase in the volume of data available. With more temporally dense constellations planned in the near future, it is imperative that automated techniques based on machine learning algorithms be developed that are able to take advantage of all the acquired data and convert latent information to actionable knowledge. However, the use of indiscriminate machine learning techniques can be problematic since there is no guarantee that the learned model makes sense from a physical standpoint. Advanced neural network techniques, collectively called 'deep leaning' algorithms have demonstrated the ability to self-learn features from a data-volume, greatly reducing the need for time-consuming feature tuning. In this book, novel deep learning algorithms and architectures are detailed for various earth observation applications using fully polarimetric SAR data based, and constrained by the principles of scattering physics.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 212 pp. Englisch. N� de r�f. du vendeur 9786139815999

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Applications of Deep Learning to Radar Polarimetry : A Physics First Approach to Machine Learning in Radar Earth Observation Applications

Shaunak de

Edit� par LAP LAMBERT Academic Publishing, 2018

ISBN 10 : 6139815991 ISBN 13 : 9786139815999

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Radar remote sensing has made significant technological and scientific advances in the past few years. Sensors and constellations are able to acquire high resolution, polarimetric, wide swath data with high temporal repetivity. This has lead to an exponential increase in the volume of data available. With more temporally dense constellations planned in the near future, it is imperative that automated techniques based on machine learning algorithms be developed that are able to take advantage of all the acquired data and convert latent information to actionable knowledge. However, the use of indiscriminate machine learning techniques can be problematic since there is no guarantee that the learned model makes sense from a physical standpoint. Advanced neural network techniques, collectively called 'deep leaning' algorithms have demonstrated the ability to self-learn features from a data-volume, greatly reducing the need for time-consuming feature tuning. In this book, novel deep learning algorithms and architectures are detailed for various earth observation applications using fully polarimetric SAR data based, and constrained by the principles of scattering physics. N� de r�f. du vendeur 9786139815999

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