國立虎尾科技大學 |

Synthetic aperture radar (SAR) data applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Synthetic aperture radar (SAR) data applications/ edited by Maciej Rysz ... [et al.].
其他作者:	Rysz, Maciej.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	x, 278 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Data Analysis and Big Data. -
電子資源:	https://doi.org/10.1007/978-3-031-21225-3
ISBN:	9783031212253

Synthetic aperture radar (SAR) data applications
Synthetic aperture radar (SAR) data applications[electronic resource] /edited by Maciej Rysz ... [et al.]. - Cham :Springer International Publishing :2022. - x, 278 p. :ill. (some col.), digital ;24 cm. - Springer optimization and its applications,v. 1991931-6836 ;. - Springer optimization and its applications ;v. 16..

End-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)

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.

ISBN: 9783031212253

Standard No.: 10.1007/978-3-031-21225-3doiSubjects--Topical Terms:

1366136
Data Analysis and Big Data.

LC Class. No.: TK6592.S95 / S96 2022

Dewey Class. No.: 621.38485

Synthetic aperture radar (SAR) data applications
LDR:03210nam a2200337 a 4500 001 1105456
003 DE-He213
005 20230118131133.0
006 m d
007 cr nn 008maaau
008 231013s2022 sz s 0 eng d
020 $a 9783031212253 $q (electronic bk.)
020 $a 9783031212246 $q (paper)
024 7 $a 10.1007/978-3-031-21225-3 $2 doi
035 $a 978-3-031-21225-3
040 $a GP $c GP
041 0 $a eng
050 4 $a TK6592.S95 $b S96 2022
072 7 $a PBU $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBU $2 thema
082 0 4 $a 621.38485 $2 23
090 $a TK6592.S95 $b S993 2022
245 0 0 $a Synthetic aperture radar (SAR) data applications $h [electronic resource] / $c edited by Maciej Rysz ... [et al.].
260 $a Cham : $c 2022. $b Springer International Publishing : $b Imprint: Springer,
300 $a x, 278 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer optimization and its applications, $x 1931-6836 ; $v v. 199
505 0 $a End-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)
520 $a 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.
650 2 4 $a Data Analysis and Big Data. $3 1366136
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistics. $3 556824
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Calculus of Variations and Optimization. $3 1366302
650 0 $a Synthetic aperture radar $x Data processing. $3 1414496
650 0 $a Synthetic aperture radar. $3 670168
700 1 $a Rysz, Maciej. $3 1414495
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Springer optimization and its applications ; $v v. 16. $3 791775
856 4 0 $u https://doi.org/10.1007/978-3-031-21225-3
950 $a Mathematics and Statistics (SpringerNature-11649)