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

Shaunak de

ISBN 10: 6139815991 / ISBN 13: 9786139815999

Editore: LAP LAMBERT Academic Publishing, 2018

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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.

Codice articolo 9786139815999

Dati bibliografici

• Titolo: Applications of Deep Learning to Radar Polarimetry : A Physics First Approach to Machine Learning in Radar Earth Observation Applications
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 6139815991
• ISBN 13: 9786139815999
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 13 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 13 millimetri di profondità
• Peso: 334 grammi

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Riassunto

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.