001452433 000__ 04263cam\a22005537i\4500
001452433 001__ 1452433
001452433 003__ OCoLC
001452433 005__ 20230310003355.0
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001452433 008__ 230130s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001452433 019__ $$a1364348093
001452433 020__ $$a9783031212253$$q(electronic bk.)
001452433 020__ $$a3031212258$$q(electronic bk.)
001452433 020__ $$z303121224X
001452433 020__ $$z9783031212246
001452433 0247_ $$a10.1007/978-3-031-21225-3$$2doi
001452433 035__ $$aSP(OCoLC)1366491454
001452433 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX
001452433 049__ $$aISEA
001452433 050_4 $$aTK6592.S95
001452433 08204 $$a621.3848/5$$223/eng/20230130
001452433 24500 $$aSynthetic aperture radar (SAR) data applications /$$cMaciej Rysz, Arsenios Tsokas, Kathleen M. Dipple, Kaitlin L. Fair, Panos M. Pardalos, editors.
001452433 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001452433 300__ $$a1 online resource (292 pages) :$$billustrations (black and white, and colour).
001452433 336__ $$atext$$btxt$$2rdacontent
001452433 337__ $$acomputer$$bc$$2rdamedia
001452433 338__ $$aonline resource$$bcr$$2rdacarrier
001452433 4901_ $$aSpringer optimization and its applications ;$$vvolume 199
001452433 5050_ $$aEnd-to-End ATR Leveraging Deep Learning (M. Kreucher) -- Change Detection in SAR Images using Deep Learning Methods (Bovolo) -- Homography Augmented Momentum Contrastive Learning for SAR Image Retrieval (M. Rysz) -- Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks (Semenov) -- A Comparison of Deep Neural Network Architectures in Aircraft Detection from SAR Imagery (L. Chen) -- Machine Learning Methods for SAR Interference Mitigation (Huang) -- Classification of SAR Images using Compact Convolutional Neural Networks (Ahishali) -- Multi-frequency Polarimetric SAR Data Analysis for Crop Type Classification using Random Forest (Mandal) -- Automatic Determination of Different Soil Types via Several Machine Learning Algorithms Employing Radarsat-2 SAR Image Polarization Coefficients (E. Acar) -- Ocean and coastal area information retrieval using SAR polarimetry (A. Buono).
001452433 506__ $$aAccess limited to authorized users.
001452433 520__ $$aThis carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information wind, wave, soil conditions, among others, are also included. .
001452433 588__ $$aDescription based on print version record.
001452433 650_0 $$aSynthetic aperture radar.
001452433 650_0 $$aSynthetic aperture radar$$xData processing.
001452433 655_0 $$aElectronic books.
001452433 7001_ $$aRysz, Maciej,$$eeditor.
001452433 7001_ $$aTsokas, Arsenios,$$eeditor.
001452433 7001_ $$aDipple, Kathleen M.$$eeditor.
001452433 7001_ $$aFair, Kaitlin L.$$eeditor.
001452433 7001_ $$aPardalos, P. M.$$q(Panos M.),$$d1954-$$eeditor.
001452433 77608 $$iPrint version:$$tSYNTHETIC APERTURE RADAR (SAR) DATA APPLICATIONS.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2023$$z303121224X$$w(OCoLC)1347696705
001452433 830_0 $$aSpringer optimization and its applications ;$$vv. 199.
001452433 852__ $$bebk
001452433 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-21225-3$$zOnline Access$$91397441.1
001452433 909CO $$ooai:library.usi.edu:1452433$$pGLOBAL_SET
001452433 980__ $$aBIB
001452433 980__ $$aEBOOK
001452433 982__ $$aEbook
001452433 983__ $$aOnline
001452433 994__ $$a92$$bISE