Synthetic aperture radar (SAR) data applications [Documento eletrónico] / edited by Maciej Rysz ... [et al.]
Language: eng.Country: Switzerland, Swiss Confederation, Cham.Publication: Cham : Springer International Publishing, 2022Description: X, 278 p. : il.ISBN: 978-3-031-21225-3.Series: Springer Optimization and Its Applications, vol. 199Subject - Topical Name: Mathematical optimization | Calculus of variations | Artificial intelligence | Statistics | Machine learning | Quantitative research Online Resources:Click here to access onlineItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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E-Books | Biblioteca NOVA FCT Online | Não Ficção | QA402.5.SPR FCT (Browse shelf(Opens below)) | 1 | Available | 96289 |
Browsing Biblioteca NOVA FCT shelves, Shelving location: Online, Collection: Não Ficção Close shelf browser (Hides shelf browser)
QA402.5.SPR FCT Introduction to unconstrained optimization with R | QA402.5.SPR FCT Nonlinear optimization | QA402.5.SPR FCT Convex and stochastic optimization | QA402.5.SPR FCT Synthetic aperture radar (SAR) data applications | QA402.5.SPR FCT Nonsmooth optimization and its applications | QA402.5.SPR FCT A derivative-free two level random search method for unconstrained optimization | QA402.5.SPR FCT Optimal coverage in wireless sensor networks |
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. .
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