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

De, Shaunak

9786209196706: Applications of Deep Learning to Radar Polarimetry: A Physics First Approach to Machine Learning in Radar Earth Observation Applications Second Edition

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ISBN 10 : 6209196705 ISBN 13 : 9786209196706

Editeur : LAP LAMBERT Academic Publishing, 2025

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2025
Langue: anglais
ISBN 10: 6209196705
ISBN 13: 9786209196706
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 Nov 2025, 2025

ISBN 10 : 6209196705 ISBN 13 : 9786209196706

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 212 pp. Englisch. N� de r�f. du vendeur 9786209196706

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Applications of Deep Learning to Radar Polarimetry

Shaunak de

Edit� par LAP LAMBERT Academic Publishing Nov 2025, 2025

ISBN 10 : 6209196705 ISBN 13 : 9786209196706

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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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 9786209196706

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