Synthetic Aperture Radar (SAR) Data Applications
Buch in deiner Nähe kaufen
Beschreibung
This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information — wind, wave, soil conditions, among others, are also included.
This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information — wind, wave, soil conditions, among others, are also included.
Provides a cohesive overview of state-of-the-art data-driven computational methods using SAR data Uses and tools employing ML, AI, SL and inversion methods in a series of SAR data applications Current applications of SAR data especially for earth observation and object detection and recognition
Autor*in
Maciej Rysz
Themen in »Synthetic Aperture Radar (SAR) Data Applications«
synthetic aperture radar SAR land cover classification forestation monitoring biomass estimation ocean surface topology oil spill detection bathymetry deformation earthquakes SAR odometry aircraft navigation machine learning statistics learning inversion models wavelet transformation