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

De, Shaunak

Softcover

ISBN 10: 6139815991 ISBN 13: 9786139815999

Verlag: LAP LAMBERT Academic Publishing, 2018

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Dr 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, specializing in the machine learning techniques for radar polarimetry that leverage scattering physics for optimal performance.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2018
• Sprache: Englisch
• ISBN 10: 6139815991
• ISBN 13: 9786139815999
• Einband: Taschenbuch
• Anzahl der Seiten: 212
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: SIA OmniScriptum Publishing; customerservice@vdm-vsg.de; ; Brivibas gatve 197; Riga; 1039; Lettland