23.8.4.4.2 Radar, SAR, Ship Detection

Chapter Contents (Back)
SAR. Radar. Ship Detection. ATR.
See also Vehicle Detection, SAR.
See also Ship Wake Detection.

Mecocci, A., Benelli, G., Garzelli, A., Bottalico, S.,
Radar image processing for ship-traffic control,
IVC(13), No. 2, March 1995, pp. 119-128.
Elsevier DOI 0401
BibRef

Musman, S., Kerr, D., Bachmann, C.,
Automatic Recognition of ISAR Ship Images,
AeroSys(32), No. 4, October 1996, pp. 1392-1404. 9611
BibRef

Maki, A.[Atsuto], Fukui, K.[Kazuhiro],
Ship identification in sequential ISAR imagery,
MVA(15), No. 3, July 2004, pp. 149-155.
Springer DOI 0407
BibRef

Margarit, G., Mallorqui, J.J., Fortuny-Guasch, J., Lopez-Martinez, C.,
Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions,
GeoRS(47), No. 4, April 2009, pp. 1224-1235.
IEEE DOI 0903
BibRef

Margarit, G., Tabasco, A.,
Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic,
GeoRS(49), No. 8, August 2011, pp. 3129-3138.
IEEE DOI 1108
BibRef
Earlier: Correction: GeoRS(51), No. 5, May 2013, pp. 3201.
IEEE DOI 1305
BibRef

Margarit, G., Barba Milanés, J., Tabasco, A.,
Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing,
RS(1), No. 3, September 2009, pp. 375-392.
DOI Link 1203
BibRef

Wang, Y., Liu, H.,
A Hierarchical Ship Detection Scheme for High-Resolution SAR Images,
GeoRS(50), No. 10, October 2012, pp. 4173-4184.
IEEE DOI 1210
BibRef

Liu, C., Gierull, C.H.,
A New Application for PolSAR Imagery in the Field of Moving Target Indication/Ship Detection,
GeoRS(45), No. 11, November 2007, pp. 3426-3436.
IEEE DOI 0709
BibRef

Brusch, S., Lehner, S., Fritz, T., Soccorsi, M., Soloviev, A., van Schie, B.,
Ship Surveillance With TerraSAR-X,
GeoRS(49), No. 3, March 2011, pp. 1092-1103.
IEEE DOI 1103
BibRef

Wang, X.L.[Xiao-Long], Chen, C.X.[Cui-Xia],
An Automatic Ship Detection Method Based on Local Gray-Level Gathering Characteristics in SAR Imagery,
ELCVIA(12), No. 1, 2013, pp. xx-yy.
DOI Link 1306
BibRef

Velotto, D., Soccorsi, M., Lehner, S.,
Azimuth Ambiguities Removal for Ship Detection Using Full Polarimetric X-Band SAR Data,
GeoRS(52), No. 1, January 2014, pp. 76-88.
IEEE DOI 1402
marine radar BibRef

Hu, C.B.[Can-Bin], Ferro-Famil, L.[Laurent], Kuang, G.Y.[Gang-Yao],
Ship Discrimination Using Polarimetric SAR Data and Coherent Time-Frequency Analysis,
RS(5), No. 12, 2013, pp. 6899-6920.
DOI Link 1412
BibRef

Atteia, G.E., Collins, M.J.[Michael J.],
On the use of compact polarimetry SAR for ship detection,
PandRS(80), No. 1, June 2013, pp. 1-9.
Elsevier DOI 1305
Ship detection; Polarimetry; Compact polarimetry; Synthetic aperture radar BibRef

Cui, Y., Yang, J., Yamaguchi, Y., Singh, G., Park, S.E., Kobayashi, H.,
On Semiparametric Clutter Estimation for Ship Detection in Synthetic Aperture Radar Images,
GeoRS(51), No. 5, May 2013, pp. 3170-3180.
IEEE DOI 1305
BibRef

Gao, G., Wang, X., Lai, T.,
Detection of Moving Ships Based on a Combination of Magnitude and Phase in Along-Track Interferometric SAR: Part I: SIMP Metric and Its Performance,
GeoRS(53), No. 7, July 2015, pp. 3565-3581.
IEEE DOI 1503
Clutter BibRef

Gao, G., Wang, X., Lai, T.,
Detection of Moving Ships Based on a Combination of Magnitude and Phase in Along-Track Interferometric SAR: Part II: Statistical Modeling and CFAR Detection,
GeoRS(53), No. 7, July 2015, pp. 3582-3599.
IEEE DOI 1503
Approximation methods BibRef

Marino, A.[Armando], Sanjuan-Ferrer, M.J.[Maria J.], Hajnsek, I.[Irena], Ouchi, K.[Kazuo],
Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms,
RS(7), No. 5, 2015, pp. 5416-5439.
DOI Link 1506
BibRef

Zhao, Y.[Ye], Zhang, M.[Min], Zhao, Y.W.[Yan-Wei], Geng, X.P.[Xu-Pu],
A Bistatic SAR Image Intensity Model for the Composite Ship-Ocean Scene,
GeoRS(53), No. 8, August 2015, pp. 4250-4258.
IEEE DOI 1506
geophysical image processing BibRef

Huang, X.J.[Xiao-Jing], Yang, W.[Wen], Zhang, H.J.[Hai-Jian], Xia, G.S.[Gui-Song],
Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision,
RS(7), No. 6, 2015, pp. 7695.
DOI Link 1507
BibRef

Touzi, R., Hurley, J., Vachon, P.W.,
Optimization of the Degree of Polarization for Enhanced Ship Detection Using Polarimetric RADARSAT-2,
GeoRS(53), No. 10, October 2015, pp. 5403-5424.
IEEE DOI 1509
electromagnetic wave polarisation BibRef

Wang, X.L.[Xiao-Long], Chen, C.X.[Cui-Xia],
A fast line-scanning-based detection algorithm for real-time SAR ship detection,
SIViP(9), No. 8, November 2015, pp. 1975-1982.
WWW Link. 1511
BibRef

Wang, X.L.[Xiao-Long], Chen, C.X.[Cui-Xia],
Adaptive ship detection in SAR images using variance WIE-based method,
SIViP(10), No. 7, October 2016, pp. 1219-1224.
WWW Link. 1609
BibRef

Tu, S.[Song], Su, Y.[Yi],
Fast and Accurate Target Detection Based on Multiscale Saliency and Active Contour Model for High-Resolution SAR Images,
GeoRS(54), No. 10, October 2016, pp. 5729-5744.
IEEE DOI 1610
image processing BibRef

Zhang, L.[Liang], Lu, S.T.[Sheng-Tao], Xiang, D.L.[De-Liang], Su, Y.[Yi],
Fast Ship Detection Based on the Superpixels for High Resolution SAR Images,
ICIVC22(172-177)
IEEE DOI 2301
Gamma distribution, Image resolution, Shape, Urban areas, Speckle, Radar polarimetry, Robustness, synthetic aperture radar (SAR), target detection BibRef

Pelich, R., Longépé, N., Mercier, G., Hajduch, G., Garello, R.,
Vessel Refocusing and Velocity Estimation on SAR Imagery Using the Fractional Fourier Transform,
GeoRS(54), No. 3, March 2016, pp. 1670-1684.
IEEE DOI 1603
Acceleration BibRef

Gao, G., Luo, Y., Ouyang, K., Zhou, S.,
Statistical Modeling of PMA Detector for Ship Detection in High-Resolution Dual-Polarization SAR Images,
GeoRS(54), No. 7, July 2016, pp. 4302-4313.
IEEE DOI 1606
Adaptation models BibRef

Gao, G., Ouyang, K., Luo, Y., Liang, S., Zhou, S.,
Scheme of Parameter Estimation for Generalized Gamma Distribution and Its Application to Ship Detection in SAR Images,
GeoRS(55), No. 3, March 2017, pp. 1812-1832.
IEEE DOI 1703
Clutter BibRef

Xu, L.[Lu], Zhang, H.[Hong], Wang, C.[Chao], Zhang, B.[Bo], Tian, S.[Sirui],
Compact Polarimetric SAR Ship Detection with m-d Decomposition Using Visual Attention Model,
RS(8), No. 9, 2016, pp. 751.
DOI Link 1610
BibRef

Fan, Q.C.[Qian-Cong], Chen, F.[Feng], Cheng, M.[Ming], Lou, S.L.[Shen-Long], Xiao, R.[Rulin], Zhang, B.[Biao], Wang, C.[Cheng], Li, J.[Jonathan],
Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Wang, S., Wang, M., Yang, S., Jiao, L.,
New Hierarchical Saliency Filtering for Fast Ship Detection in High-Resolution SAR Images,
GeoRS(55), No. 1, January 2017, pp. 351-362.
IEEE DOI 1701
geophysical techniques BibRef

Gao, G.[Gui], Shi, G.T.[Gong-Tao],
CFAR Ship Detection in Nonhomogeneous Sea Clutter Using Polarimetric SAR Data Based on the Notch Filter,
GeoRS(55), No. 8, August 2017, pp. 4811-4824.
IEEE DOI 1708
Clutter, Data models, Detectors, Marine vehicles, Scattering, Sea measurements, Synthetic aperture radar, Constant false alarm rate (CFAR), polarization, ship detection, synthetic, aperture, radar, (SAR) BibRef

Gao, G.[Gui], Shi, G.T.[Gong-Tao],
Ship Detection in Dual-Channel ATI-SAR Based on the Notch Filter,
GeoRS(55), No. 8, August 2017, pp. 4795-4810.
IEEE DOI 1708
Clutter, Covariance matrices, Interferometry, Marine vehicles, Oceans, Receivers, Synthetic aperture radar, Along-track interferometry (ATI), detection, ship, synthetic, aperture, radar, (SAR) BibRef

Gao, G.[Gui], Gao, S.[Sheng], He, J.[Juan], Li, G.S.[Gao-Sheng],
Ship Detection Using Compact Polarimetric SAR Based on the Notch Filter,
GeoRS(56), No. 9, September 2018, pp. 5380-5393.
IEEE DOI 1809
Marine vehicles, Synthetic aperture radar, Feature extraction, Scattering, Image reconstruction, Clutter, Oceans, synthetic aperture radar (SAR) BibRef

Gao, G.[Gui], Gao, S.[Sheng], He, J.[Juan], Li, G.S.[Gao-Sheng],
Adaptive Ship Detection in Hybrid-Polarimetric SAR Images Based on the Power-Entropy Decomposition,
GeoRS(56), No. 9, September 2018, pp. 5394-5407.
IEEE DOI 1809
Synthetic aperture radar, Marine vehicles, Feature extraction, Spaceborne radar, Entropy, Image reconstruction, synthetic aperture radar (SAR) BibRef

Renga, A., Moccia, A.,
Use of Doppler Parameters for Ship Velocity Computation in SAR Images,
GeoRS(54), No. 7, July 2016, pp. 3995-4011.
IEEE DOI 1606
Azimuth BibRef

Zhu, J.W.[Ji-Wei], Qiu, X.L.[Xiao-Lan], Pan, Z.X.[Zong-Xu], Zhang, Y.T.[Yue-Ting], Lei, B.[Bin],
An Improved Shape Contexts Based Ship Classification in SAR Images,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Greidanus, H.[Harm], Alvarez, M.[Marlene], Santamaria, C.[Carlos], Thoorens, F.X.[François-Xavier], Kourti, N.[Naouma], Argentieri, P.[Pietro],
The SUMO Ship Detector Algorithm for Satellite Radar Images,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Xi, Y.Y.[Yu-Yang], Lang, H.T.[Hai-Tao], Tao, Y.H.[Yun-Hong], Huang, L.[Lin], Pei, Z.J.[Zi-Jun],
Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Kang, M.[Miao], Ji, K.F.[Ke-Feng], Leng, X.G.[Xiang-Guang], Lin, Z.[Zhao],
Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Santamaria, C.[Carlos], Alvarez, M.[Marlene], Greidanus, H.[Harm], Syrris, V.[Vasileios], Soille, P.[Pierre], Argentieri, P.[Pietro],
Mass Processing of Sentinel-1 Images for Maritime Surveillance,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Ma, F.[Feng], Chen, Y.W.[Yu-Wang], Yan, X.P.[Xin-Ping], Chu, X.M.[Xiu-Min], Wang, J.[Jin],
Target recognition for coastal surveillance based on radar images and generalised Bayesian inference,
IET-ITS(12), No. 2, March 2018, pp. 103-112.
DOI Link 1801
BibRef

Lin, H.P.[Hui-Ping], Song, S.L.[Sheng-Li], Yang, J.[Jian],
Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learning with Structured Incoherent Constraints in SAR Images,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Lin, H.P.[Hui-Ping], Chen, H.[Hang], Wang, H.M.[Hong-Miao], Yin, J.J.[Jun-Jun], Yang, J.[Jian],
Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Gierull, C.H., Sikaneta, I.,
A Compound-Plus-Noise Model for Improved Vessel Detection in Non-Gaussian SAR Imagery,
GeoRS(56), No. 3, March 2018, pp. 1444-1453.
IEEE DOI 1804
computational complexity, probability, radar clutter, radar detection, radar imaging, synthetic aperture radar, synthetic aperture radar (SAR) BibRef

Rikka, S.[Sander], Pleskachevsky, A.[Andrey], Jacobsen, S.[Sven], Alari, V.[Victor], Uiboupin, R.[Rivo],
Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhang, T.[Tao], Marino, A.[Armando], Xiong, H.L.[Hui-Lin], Yu, W.X.[Wen-Xian],
A Ship Detector Applying Principal Component Analysis to the Polarimetric Notch Filter,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Wang, Y., Chen, X.,
3-D Interferometric Inverse Synthetic Aperture Radar Imaging of Ship Target With Complex Motion,
GeoRS(56), No. 7, July 2018, pp. 3693-3708.
IEEE DOI 1807
Doppler radar, Imaging, Marine vehicles, Radar imaging, Radar scattering, Solid modeling, ship target BibRef

Similä, M.[Markku], Lensu, M.[Mikko],
Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Li, B., Liu, B., Guo, W., Zhang, Z., Yu, W.,
Ship Size Extraction for Sentinel-1 Images Based on Dual-Polarization Fusion and Nonlinear Regression: Push Error Under One Pixel,
GeoRS(56), No. 8, August 2018, pp. 4887-4905.
IEEE DOI 1808
gradient methods, image fusion, marine radar, radar imaging, regression analysis, ships, synthetic aperture radar, synthetic aperture radar (SAR) image BibRef

Shi, S., Shui, P.,
Sea-Surface Floating Small Target Detection by One-Class Classifier in Time-Frequency Feature Space,
GeoRS(56), No. 11, November 2018, pp. 6395-6411.
IEEE DOI 1811
Clutter, Feature extraction, Time series analysis, Detectors, Doppler effect, Radar cross-sections, one-class classifier BibRef

Hwang, J.I.[Jeong-In], Jung, H.S.[Hyung-Sup],
Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band SAR Satellite Images,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Ma, M.Y.[Meng-Yuan], Chen, J.[Jie], Liu, W.[Wei], Yang, W.[Wei],
Ship Classification and Detection Based on CNN Using GF-3 SAR Images,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zhang, Y.[Ying], Xiong, W.[Wei], Dong, X.[Xichao], Hu, C.[Cheng], Sun, Y.[Yang],
GRFT-Based Moving Ship Target Detection and Imaging in Geosynchronous SAR,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Leng, X., Ji, K., Zhou, S., Xing, X., Zou, H.,
Discriminating Ship From Radio Frequency Interference Based on Noncircularity and Non-Gaussianity in Sentinel-1 SAR Imagery,
GeoRS(57), No. 1, January 2019, pp. 352-363.
IEEE DOI 1901
Marine vehicles, Synthetic aperture radar, Oceans, Instruments, Sensors, Image resolution, Radiofrequency interference, synthetic aperture radar (SAR) BibRef

Snapir, B.[Boris], Waine, T.W.[Toby W.], Biermann, L.[Lauren],
Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Liu, N.Y.[Neng-Yuan], Cao, Z.J.[Zong-Jie], Cui, Z.Y.[Zong-Yong], Pi, Y.M.[Yi-Ming], Dang, S.H.[Si-Hang],
Multi-Scale Proposal Generation for Ship Detection in SAR Images,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Cui, Z.Y.[Zong-Yong], Tang, C.[Cui], Cao, Z.J.[Zong-Jie], Liu, N.Y.[Neng-Yuan],
D-ATR for SAR Images Based on Deep Neural Networks,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Cui, Z.Y.[Zong-Yong], Dang, S.H.[Si-Hang], Cao, Z.J.[Zong-Jie], Wang, S.F.[Si-Fei], Liu, N.Y.[Neng-Yuan],
SAR Target Recognition in Large Scene Images via Region-Based Convolutional Neural Networks,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Wang, J.[Jun], Zheng, T.[Tong], Lei, P.[Peng], Bai, X.[Xiao],
A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Renga, A., Graziano, M.D., Moccia, A.,
Segmentation of Marine SAR Images by Sublook Analysis and Application to Sea Traffic Monitoring,
GeoRS(57), No. 3, March 2019, pp. 1463-1477.
IEEE DOI 1903
geophysical image processing, image segmentation, oceanographic regions, remote sensing by radar, ships, synthetic aperture radar (SAR) BibRef

Wang, Y.Y.[Yuan-Yuan], Wang, C.[Chao], Zhang, H.[Hong], Dong, Y.[Yingbo], Wei, S.[Sisi],
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Chang, Y.L.[Yang-Lang], Anagaw, A.[Amare], Chang, L.[Lena], Wang, Y.C.[Yi Chun], Hsiao, C.Y.[Chih-Yu], Lee, W.H.[Wei-Hong],
Ship Detection Based on YOLOv2 for SAR Imagery,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Gierull, C.H.,
Demystifying the Capability of Sublook Correlation Techniques for Vessel Detection in SAR Imagery,
GeoRS(57), No. 4, April 2019, pp. 2031-2042.
IEEE DOI 1904
Doppler radar, image texture, marine radar, optical correlation, radar detection, radar imaging, ships, statistical analysis, synthetic aperture radar (SAR) BibRef

Filippo, B.[Biondi],
COSMO-SkyMed Staring Spotlight SAR Data for Micro-Motion and Inclination Angle Estimation of Ships by Pixel Tracking and Convex Optimization,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Salembier, P., Liesegang, S., López-Martínez, C.,
Ship Detection in SAR Images Based on Maxtree Representation and Graph Signal Processing,
GeoRS(57), No. 5, May 2019, pp. 2709-2724.
IEEE DOI 1905
filtering theory, geophysical image processing, image representation, learning (artificial intelligence), tree filter BibRef

Zhang, T.[Tao], Ji, J.S.[Jin-Sheng], Li, X.F.[Xiao-Feng], Yu, W.X.[Wen-Xian], Xiong, H.L.[Hui-Lin],
Ship Detection From PolSAR Imagery Using the Complete Polarimetric Covariance Difference Matrix,
GeoRS(57), No. 5, May 2019, pp. 2824-2839.
IEEE DOI 1905
covariance matrices, geophysical signal processing, notch filters, object detection, radar clutter, radar imaging, target-to-clutter ratio (TCR) BibRef

Zhang, T.[Tao], Jiang, L.F.[Lin-Feng], Xiang, D.L.[De-Liang], Ban, Y.F.[Yi-Fang], Pei, L.[Ling], Xiong, H.L.[Hui-Lin],
Ship detection from PolSAR imagery using the ambiguity removal polarimetric notch filter,
PandRS(157), 2019, pp. 41-58.
Elsevier DOI 1911
PolSAR, Ship detection, Azimuth ambiguity removal, GP-PNF, PSH, Depolarized energy ratio of targets BibRef

Zhang, J.Z.[Jia-Zhi], Zhang, X.[Xin], Deng, W.B.[Wei-Bo], Ye, L.[Lei], Yang, Q.A.[Qi-Ang],
A Geometric Barycenter-Based Clutter Suppression Method for Ship Detection in HF Mixed-Mode Surface Wave Radar,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Pelich, R.[Ramona], Chini, M.[Marco], Hostache, R.[Renaud], Matgen, P.[Patrick], Lopez-Martinez, C.[Carlos], Nuevo, M.[Miguel], Ries, P.[Philippe], Eiden, G.[Gerd],
Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
dual-polarimetric descriptors. Ships. BibRef

Chen, S.Y.[Shi-Yuan], Li, X.J.[Xiao-Jiang],
A new CFAR algorithm based on variable window for ship target detection in SAR images,
SIViP(13), No. 4, June 2019, pp. 779-786.
WWW Link. 1906
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling],
High-Speed Ship Detection in SAR Images Based on a Grid Convolutional Neural Network,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Joshi, S.K.[Sushil Kumar], Baumgartner, S.V.[Stefan V.], da Silva, A.B.C.[Andre B. C.], Krieger, G.[Gerhard],
Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Back, M.Y.[Min-Young], Kim, D.H.[Dong-Han], Kim, S.W.[Sang-Wan], Won, J.S.[Joong-Sun],
Two-Dimensional Ship Velocity Estimation Based on KOMPSAT-5 Synthetic Aperture Radar Data,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Santi, F., Pieralice, F., Pastina, D.,
Joint Detection and Localization of Vessels at Sea With a GNSS-Based Multistatic Radar,
GeoRS(57), No. 8, August 2019, pp. 5894-5913.
IEEE DOI 1908
Doppler radar, passive radar, radar receivers, radio receivers, radio transmitters, satellite navigation, ship targets, ship localization BibRef

Lang, H., Xi, Y., Zhang, X.,
Ship Detection in High-Resolution SAR Images by Clustering Spatially Enhanced Pixel Descriptor,
GeoRS(57), No. 8, August 2019, pp. 5407-5423.
IEEE DOI 1908
image resolution, object detection, radar imaging, ships, synthetic aperture radar, ship detection, synthetic aperture radar (SAR) BibRef

Biondi, F.[Filippo], Addabbo, P.[Pia], Orlando, D.[Danilo], Clemente, C.[Carmine],
Micro-Motion Estimation of Maritime Targets Using Pixel Tracking in Cosmo-Skymed Synthetic Aperture Radar Data: An Operative Assessment,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Ding, Z., Zhang, T., Li, Y., Li, G., Dong, X., Zeng, T., Ke, M.,
A Ship ISAR Imaging Algorithm Based on Generalized Radon-Fourier Transform With Low SNR,
GeoRS(57), No. 9, September 2019, pp. 6385-6396.
IEEE DOI 1909
Marine vehicles, Imaging, Signal to noise ratio, Signal processing algorithms, Radar imaging, Doppler effect, noncooperative ship BibRef

Leng, X., Ji, K., Zhou, S., Xing, X.,
Ship Detection Based on Complex Signal Kurtosis in Single-Channel SAR Imagery,
GeoRS(57), No. 9, September 2019, pp. 6447-6461.
IEEE DOI 1909
Marine vehicles, Radar polarimetry, Synthetic aperture radar, Clutter, Gaussian distribution, Proposals, Sensitivity, synthetic aperture radar (SAR) BibRef

Cao, C.H.[Cheng-Hui], Zhang, J.[Jie], Meng, J.M.[Jun-Min], Zhang, X.[Xi], Mao, X.P.[Xing-Peng],
Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Yang, J.Y.[Jun-Ying], Qiu, X.L.[Xiao-Lan], Zhong, L.H.[Li-Hua], Shang, M.Y.[Ming-Yang], Ding, C.B.[Chi-Biao],
A Simultaneous Imaging Scheme of Stationary Clutter and Moving Targets for Maritime Scenarios with the First Chinese Dual-Channel Spaceborne SAR Sensor,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Shi, J.[Jun], Wei, S.J.[Shun-Jun],
Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Cui, Z., Li, Q., Cao, Z., Liu, N.,
Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images,
GeoRS(57), No. 11, November 2019, pp. 8983-8997.
IEEE DOI 1911
Marine vehicles, Feature extraction, Radar polarimetry, Synthetic aperture radar, Radar imaging, Semantics, synthetic aperture radar (SAR) BibRef

Sommer, A., Ostermann, J.,
Backprojection Subimage Autofocus of Moving Ships for Synthetic Aperture Radar,
GeoRS(57), No. 11, November 2019, pp. 8383-8393.
IEEE DOI 1911
Marine vehicles, Synthetic aperture radar, Antennas, Image reconstruction, Radar imaging, Spaceborne radar, synthetic aperture radar (SAR) BibRef

Gao, F.[Fei], Shi, W.[Wei], Wang, J.[Jun], Yang, E.[Erfu], Zhou, H.Y.[Hui-Yu],
Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Fan, W.W.[Wei-Wei], Zhou, F.[Feng], Bai, X.[Xueru], Tao, M.L.[Ming-Liang], Tian, T.[Tian],
Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Ai, J., Tian, R., Luo, Q., Jin, J., Tang, B.,
Multi-Scale Rotation-Invariant Haar-Like Feature Integrated CNN-Based Ship Detection Algorithm of Multiple-Target Environment in SAR Imagery,
GeoRS(57), No. 12, December 2019, pp. 10070-10087.
IEEE DOI 1912
Marine vehicles, Feature extraction, Clutter, Synthetic aperture radar, Radar polarimetry, Correlation, constant false alarm rate (CFAR) detector (TCS-JCFAR)-based prescreening BibRef

Dechesne, C.[Clément], Lefčvre, S.[Sébastien], Vadaine, R.[Rodolphe], Hajduch, G.[Guillaume], Fablet, R.[Ronan],
Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
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Liu, G.W.[Gen-Wang], Zhang, X.[Xi], Meng, J.M.[Jun-Min],
A Small Ship Target Detection Method Based on Polarimetric SAR,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
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Liang, Y.[Yi], Sun, K.[Kun], Zeng, Y.G.[Yu-Gui], Li, G.F.[Guo-Fei], Xing, M.D.[Meng-Dao],
An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
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Wei, S.J.[Shun-Jun], Su, H.[Hao], Ming, J.[Jing], Wang, C.[Chen], Yan, M.[Min], Kumar, D.[Durga], Shi, J.[Jun], Zhang, X.L.[Xiao-Ling],
Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Liu, T., Zhang, J., Gao, G., Yang, J., Marino, A.,
CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter,
GeoRS(58), No. 1, January 2020, pp. 58-81.
IEEE DOI 2001
Marine vehicles, Speckle, Covariance matrices, Clutter, Synthetic aperture radar, Probability density function, synthetic aperture radar BibRef

Tian, M., Yang, Z., Duan, C., Liao, G., Liu, Y., Wang, C., Huang, P.,
A Method for Active Marine Target Detection Based on Complex Interferometric Dissimilarity in Dual-Channel ATI-SAR Systems,
GeoRS(58), No. 1, January 2020, pp. 251-267.
IEEE DOI 2001
Object detection, Synthetic aperture radar, Measurement, Marine vehicles, Clutter, Radar polarimetry, synthetic aperture radar (SAR) BibRef

Lu, H., Li, Y., Li, H., Lv, R., Lang, L., Li, Q., Song, G., Li, P., Wang, K., Xue, L., Zhu, D.,
Ship Detection by an Airborne Passive Interferometric Microwave Sensor (PIMS),
GeoRS(58), No. 4, April 2020, pp. 2682-2694.
IEEE DOI 2004
Airborne, interferometric, microwave sensor, passive, ship detection BibRef

Guo, R.[Rui], Cui, J.Y.[Jing-Yu], Jing, G.B.[Guo-Bin], Zhang, S.X.[Shuang-Xi], Xing, M.D.[Meng-Dao],
Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
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Li, Y.Z.[Yong-Zhen], Quan, S.[Sinong], Xiang, D.L.[De-Liang], Wang, W.[Wei], Hu, C.B.[Can-Bin], Liu, Y.[Yemin], Wang, X.S.[Xue-Song],
Ship Recognition from Chaff Clouds with Sophisticated Polarimetric Decomposition,
RS(12), No. 11, 2020, pp. xx-yy.
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Chen, S.Q.[Shi-Qi], Zhang, J.[Jun], Zhan, R.H.[Rong-Hui],
R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images,
RS(12), No. 12, 2020, pp. xx-yy.
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Xiong, W.[Wei], Zhang, Y.[Ying], Dong, X.[Xichao], Cui, C.[Chang], Liu, Z.[Zheng], Xiong, M.H.[Ming-Hui],
A Novel Ship Imaging Method with Multiple Sinusoidal Functions to Match Rotation Effects in Geosynchronous SAR,
RS(12), No. 14, 2020, pp. xx-yy.
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Gao, F.[Fei], He, Y.S.[Yi-Shan], Wang, J.[Jun], Hussain, A.[Amir], Zhou, H.Y.[Hui-Yu],
Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images,
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Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Shi, J.[Jun], Wei, S.J.[Shun-Jun],
HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery,
PandRS(167), 2020, pp. 123-153.
Elsevier DOI 2008
HyperLi-Net, Deep learning, Ship detection, Synthetic Aperture Radar (SAR), High-speed, High-accurate BibRef

Zhang, T.W.[Tian-Wen], Zeng, T.J.[Tian-Jiao], Zhang, X.L.[Xiao-Ling],
Synthetic Aperture Radar (SAR) Meets Deep Learning,
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Jin, K., Chen, Y., Xu, B., Yin, J., Wang, X., Yang, J.,
A Patch-to-Pixel Convolutional Neural Network for Small Ship Detection With PolSAR Images,
GeoRS(58), No. 9, September 2020, pp. 6623-6638.
IEEE DOI 2008
Feature extraction, Marine vehicles, Object detection, Optical imaging, Optical sensors, Synthetic aperture radar, ship detection BibRef

Wang, X.Q.[Xue-Qian], Li, G.[Gang], Zhang, X., He, Y.[You],
Ship Detection in SAR Images via Local Contrast of Fisher Vectors,
GeoRS(58), No. 9, September 2020, pp. 6467-6479.
IEEE DOI 2008
Marine vehicles, Radar polarimetry, Detectors, Clutter, Synthetic aperture radar, Detection algorithms, synthetic aperture radar (SAR) BibRef

Wang, X.Q.[Xue-Qian], Li, G.[Gang], Plaza, A.[Antonio], He, Y.[You],
Ship Detection in SAR Images via Enhanced Nonnegative Sparse Locality-Representation of Fisher Vectors,
GeoRS(59), No. 11, November 2021, pp. 9424-9438.
IEEE DOI 2111
Marine vehicles, Radar polarimetry, Detectors, Clutter, Feature extraction, Synthetic aperture radar, Training, synthetic aperture radar (SAR) BibRef

Liu, T., Yang, Z., Marino, A., Gao, G., Yang, J.,
Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar,
GeoRS(58), No. 9, September 2020, pp. 6731-6747.
IEEE DOI 2008
Marine vehicles, Clutter, Detectors, Covariance matrices, Synthetic aperture radar, Training, truncated statistics (TS) BibRef

Graziano, M.D.[Maria Daniela], Renga, A.[Alfredo], Moccia, A.[Antonio],
Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
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Kuang, C., Wang, C., Wen, B., Hou, Y., Lai, Y.,
An Improved CA-CFAR Method for Ship Target Detection in Strong Clutter Using UHF Radar,
SPLetters(27), 2020, pp. 1445-1449.
IEEE DOI 2009
Clutter, Rivers, Marine vehicles, Doppler effect, Doppler radar, Radar cross-sections, Extended target, improved CA-CFAR method, UHF radar BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Ke, X.[Xiao], Zhan, X.[Xu], Shi, J.[Jun], Wei, S.J.[Shun-Jun], Pan, D.[Dece], Li, J.W.[Jian-Wei], Su, H.[Hao], Zhou, Y.[Yue], Kumar, D.[Durga],
LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
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Zhang, T., Yang, Z., Gan, H., Xiang, D., Zhu, S., Yang, J.,
PolSAR Ship Detection Using the Joint Polarimetric Information,
GeoRS(58), No. 11, November 2020, pp. 8225-8241.
IEEE DOI 2011
Marine vehicles, Scattering, Sea surface, Rough surfaces, Surface roughness, Detectors, Covariance matrices, surface scattering BibRef

Ding, F.[Fan], Zhao, C.[Chen], Chen, Z.Z.[Ze-Zong], Li, J.[Jian],
Sea Echoes for Airborne HF/VHF Radar: Mathematical Model and Simulation,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Yang, J.Y.[Jun-Ying], Qiu, X.L.[Xiao-Lan], Shang, M.Y.[Ming-Yang], Zhong, L.H.[Li-Hua], Ding, C.[Chibiao],
A Method of Marine Moving Targets Detection in Multi-Channel ScanSAR System,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
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Yang, R.[Rong], Wang, R.[Robert], Deng, Y.K.[Yun-Kai], Jia, X.X.[Xiao-Xue], Zhang, H.[Heng],
Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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Fu, J.M.[Jia-Mei], Sun, X.[Xian], Wang, Z.R.[Zhi-Rui], Fu, K.[Kun],
An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images,
GeoRS(59), No. 2, February 2021, pp. 1331-1344.
IEEE DOI 2101
Marine vehicles, Feature extraction, Synthetic aperture radar, Detectors, Radar polarimetry, Scattering, Semantics, synthetic aperture radar (SAR) BibRef

Chen, J.S., Dao, D.T., Chien, H.,
Ship Echo Identification Based on Norm-Constrained Adaptive Beamforming for an Arrayed High-Frequency Coastal Radar,
GeoRS(59), No. 2, February 2021, pp. 1143-1153.
IEEE DOI 2101
Marine vehicles, Brightness, Array signal processing, Radar detection, Sea measurements, Radar antennas, ship detection BibRef

Guo, H.Y.[Hao-Yuan], Yang, X.[Xi], Wang, N.N.[Nan-Nan], Gao, X.B.[Xin-Bo],
A CenterNet++ model for ship detection in SAR images,
PR(112), 2021, pp. 107787.
Elsevier DOI 2102
Ship detection, Synthetic aperture radar (SAR), Deep learning BibRef

Pastina, D., Santi, F., Pieralice, F., Antoniou, M., Cherniakov, M.,
Passive Radar Imaging of Ship Targets With GNSS Signals of Opportunity,
GeoRS(59), No. 3, March 2021, pp. 2627-2642.
IEEE DOI 2103
Marine vehicles, Radar imaging, Passive radar, Global navigation satellite system, Satellites, Receivers, passive radar imaging BibRef

Tang, G.[Gang], Zhuge, Y.C.[Yi-Chao], Claramunt, C.[Christophe], Men, S.Y.[Shao-Yang],
N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

He, J., Wang, Y., Liu, H.,
Ship Classification in Medium-Resolution SAR Images via Densely Connected Triplet CNNs Integrating Fisher Discrimination Regularized Metric Learning,
GeoRS(59), No. 4, April 2021, pp. 3022-3039.
IEEE DOI 2104
Marine vehicles, Synthetic aperture radar, Measurement, Training, Task analysis, Scattering, Surveillance, synthetic aperture radar (SAR) BibRef

Lin, C.L.[Chih-Lung], Chen, T.P.[Tsung-Pin], Fan, K.C.[Kuo-Chin], Cheng, H.Y.[Hsu-Yung], Chuang, C.H.[Chi-Hung],
Radar High-Resolution Range Profile Ship Recognition Using Two-Channel Convolutional Neural Networks Concatenated with Bidirectional Long Short-Term Memory,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
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Yang, Z.Q.[Zhi-Qing], Tang, J.J.[Jian-Jiang], Zhou, H.[Hao], Xu, X.J.[Xin-Jun], Tian, Y.W.[Ying-Wei], Wen, B.Y.[Bi-Yang],
Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Zhou, B.B.[Bin-Bin], Qi, X.Y.[Xiang-Yang], Zhang, J.H.[Jia-Huan], Zhang, H.[Heng],
Effect of 6-DOF Oscillation of Ship Target on SAR Imaging,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
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Xu, P.[Pan], Li, Q.Y.[Qing-Yang], Zhang, B.[Bo], Wu, F.[Fan], Zhao, K.[Ke], Du, X.[Xin], Yang, C.K.[Can-Kun], Zhong, R.F.[Ruo-Fei],
On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Lv, Y.[Yini], Zhong, L.H.[Li-Hua], Qiu, X.L.[Xiao-Lan], Yuan, X.Z.[Xin-Zhe], Yang, J.[Junying], Hu, Y.X.[Yu-Xin], Ding, C.[Chibiao],
Improving the Image Quality of Moving Ships for GF-3NG Based on Simultaneous AIS Information,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Guan, Y.[Yanan], Zhang, J.[Jie], Zhang, X.[Xi], Li, Z.W.[Zhong-Wei], Meng, J.M.[Jun-Min], Liu, G.W.[Gen-Wang], Bao, M.[Meng], Cao, C.H.[Cheng-Hui],
Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
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Liu, T.[Tao], Yang, Z.Y.[Zi-Yuan], Marino, A.[Armando], Gao, G.[Gui], Yang, J.[Jian],
PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix,
GeoRS(59), No. 6, June 2021, pp. 4874-4887.
IEEE DOI 2106
Marine vehicles, Covariance matrices, Detectors, Correlation, Synthetic aperture radar, Clutter, Scattering, polarimetric whitening filter (PWF) BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling],
Injection of Traditional Hand-Crafted Features into Modern CNN-Based Models for SAR Ship Classification: What, Why, Where, and How,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Wang, X.Q.[Xue-Qian], Li, G.[Gang], Zhang, X.P.[Xiao-Ping], He, Y.[You],
A Fast CFAR Algorithm Based on Density-Censoring Operation for Ship Detection in SAR Images,
SPLetters(28), 2021, pp. 1085-1089.
IEEE DOI 2106
Radar polarimetry, Clutter, Detectors, Marine vehicles, Reflectivity, Signal processing algorithms, Feature extraction, Density, constant false alarm rate BibRef

Chen, S.W.[Si-Wei], Cui, X.C.[Xing-Chao], Wang, X.S.[Xue-Song], Xiao, S.P.[Shun-Ping],
Speckle-Free SAR Image Ship Detection,
IP(30), 2021, pp. 5969-5983.
IEEE DOI 2107
Marine vehicles, Speckle, Synthetic aperture radar, Detectors, Radar polarimetry, Spaceborne radar, Radar imaging, speckle-free BibRef

Yu, L.[Lei], Wu, H.Y.[Hao-Yu], Zhong, Z.[Zhi], Zheng, L.Y.[Li-Ying], Deng, Q.Y.[Qiu-Yue], Hu, H.C.[Hai-Cheng],
TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Jia, X.L.[Xin-Lin], Song, H.J.[Hong-Jun], He, W.J.[Wen-Jing],
A Novel Method for Refocusing Moving Ships in SAR Images via ISAR Technique,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
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Sun, K.[Kun], Liang, Y.[Yi], Ma, X.R.[Xiao-Rui], Huai, Y.Y.[Yuan-Yuan], Xing, M.D.[Meng-Dao],
DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Ke, X.[Xiao],
Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Tong, X.[Xuyao], Bao, M.[Min], Sun, G.C.[Guang-Cai], Han, L.[Liang], Zhang, Y.[Yu], Xing, M.D.[Meng-Dao],
Refocusing of Moving Ships in Squint SAR Images Based on Spectrum Orthogonalization,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Xu, Y.J.[Yong-Jie], Lang, H.T.[Hai-Tao],
Ship Classification in SAR Images With Geometric Transfer Metric Learning,
GeoRS(59), No. 8, August 2021, pp. 6799-6813.
IEEE DOI 2108
Marine vehicles, Measurement, Synthetic aperture radar, Task analysis, Electronics packaging, Optimization, transfer metric learning (TML) BibRef

Jiang, W.Q.[Wang-Qiang], Wang, L.Y.[Liu-Ying], Li, X.Z.[Xin-Zhuo], Liu, G.[Gu], Zhang, M.[Min],
Simulation of a Wideband Radar Echo of a Target on a Dynamic Sea Surface,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Geng, X.M.[Xiao-Meng], Zhao, L.[Lingli], Shi, L.[Lei], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Sun, W.D.[Wei-Dong],
Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Li, J.W.[Jian-Wei], Xu, X.W.[Xiao-Wo], Wang, B.Y.[Bao-You], Zhan, X.[Xu], Xu, Y.Q.[Yan-Qin], Ke, X.[Xiao], Zeng, T.J.[Tian-Jiao], Su, H.[Hao], Ahmad, I.[Israr], Pan, D.[Dece], Liu, C.[Chang], Zhou, Y.[Yue], Shi, J.[Jun], Wei, S.[Shunjun],
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

WWW Link. Dataset, Ships. BibRef

Li, H.L.[Hao-Liang], Cui, X.C.[Xing-Chao], Chen, S.W.[Si-Wei],
PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Chen, X.L.[Xiao-Long], Guan, J.[Jian], Mu, X.Q.[Xiao-Qian], Wang, Z.[Zhigao], Liu, N.[Ningbo], Wang, G.Q.[Guo-Qing],
Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Zhu, D.[Dong], Wang, X.Q.[Xue-Qian], Cheng, Y.[Yayun], Li, G.[Gang],
Vessel Target Detection in Spaceborne-Airborne Collaborative SAR Images via Proposal and Polarization Fusion,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
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Sun, Z.Z.[Zhong-Zhen], Leng, X.G.[Xiang-Guang], Lei, Y.[Yu], Xiong, B.[Boli], Ji, K.[Kefeng], Kuang, G.Y.[Gang-Yao],
BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Liu, C.[Chang], Shi, J.[Jun], Wei, S.J.[Shun-Jun], Ahmad, I.[Israr], Zhan, X.[Xu], Zhou, Y.[Yue], Pan, D.[Dece], Li, J.W.[Jian-Wei], Su, H.[Hao],
Balance learning for ship detection from synthetic aperture radar remote sensing imagery,
PandRS(182), 2021, pp. 190-207.
Elsevier DOI 2112
Synthetic aperture radar (SAR), Ship detection, Marine surveillance, Imbalance problems and solutions, Balance learning network (BL-Net) BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling],
A polarization fusion network with geometric feature embedding for SAR ship classification,
PR(123), 2022, pp. 108365.
Elsevier DOI 2112
Synthetic aperture radar (SAR), Ship classification, Convolutional neural network, Polarization fusion (PF), Geometric feature embedding (GFE) BibRef

Xu, X.[Xiaowo], Zhang, X.L.[Xiao-Ling], Shao, Z.K.[Zi-Kang], Shi, J.[Jun], Wei, S.[Shunjun], Zhang, T.W.[Tian-Wen], Zeng, T.J.[Tian-Jiao],
A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
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Zhao, D.[Danpei], Zhu, C.[Chunbo], Qi, J.[Jing], Qi, X.H.[Xin-Hu], Su, Z.H.[Zhen-Hua], Shi, Z.W.[Zhen-Wei],
Synergistic Attention for Ship Instance Segmentation in SAR Images,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Xu, L.[Libo], Pang, C.Y.[Chao-Yi], Guo, Y.[Yan], Shu, Z.Y.[Zhen-Yu],
Combinational Fusion and Global Attention of the Single-Shot Method for Synthetic Aperture Radar Ship Detection,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
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Zhang, P.P.[Pan-Pan], Luo, H.B.[Hai-Bo], Ju, M.[Moran], He, M.[Miao], Chang, Z.[Zheng], Hui, B.[Bin],
Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images,
GeoRS(60), 2022, pp. 1-9.
IEEE DOI 2112
Marine vehicles, Feature extraction, Synthetic aperture radar, Detectors, Radar polarimetry, Visualization, Focusing, saliency map BibRef

Xu, X.B.[Xin-Bo], Su, F.[Fulin], Gao, J.J.[Jian-Jun], Jin, X.F.[Xin-Fei],
High-Squint SAR Imaging of Maritime Ship Targets,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Marine vehicles, Imaging, Azimuth, Sea state, Chirp, Radar imaging, Doppler effect, High-squint synthetic aperture radar (HS-SAR), modified range-Doppler (RD) algorithm BibRef

Li, D.[Dong], Liang, Q.H.[Quan-Huan], Liu, H.Q.[Hong-Qing], Liu, Q.H.[Qing-Hua], Liu, H.J.[Hai-Jun], Liao, G.S.[Gui-Sheng],
A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Marine vehicles, Feature extraction, Synthetic aperture radar, Radar polarimetry, Detectors, Object detection, synthetic aperture radar (SAR) BibRef

Ren, Y.[Yibin], Li, X.F.[Xiao-Feng], Xu, H.[Huan],
A Deep Learning Model to Extract Ship Size From Sentinel-1 SAR Images,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Marine vehicles, Radar polarimetry, Feature extraction, Synthetic aperture radar, Data mining, Radar imaging, Oceans, synthetic aperture radar (SAR) image BibRef

Lei, S.L.[Song-Lin], Lu, D.D.[Dong-Dong], Qiu, X.L.[Xiao-Lan], Ding, C.[Chibiao],
SRSDD-v1.0: A High-Resolution SAR Rotation Ship Detection Dataset,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
Dataset, Ship Detection. BibRef

Lan, X.[Xiang], Wang, L.Y.[Liu-Ying], Li, J.X.[Jin-Xing], Jiang, W.Q.[Wang-Qiang], Zhang, M.[Min],
Maritime Multiple Moving Target Detection Using Multiple-BDS-Based Radar: Doppler Phase Compensation and Resolution Improvement,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Yu, J.M.[Ji-Min], Zhou, G.Y.[Guang-Yu], Zhou, S.B.[Shang-Bo], Qin, M.W.[Mao-Wei],
A Fast and Lightweight Detection Network for Multi-Scale SAR Ship Detection under Complex Backgrounds,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Zhang, Q.[Qian], Huo, W.[Weibo], Pei, J.F.[Ji-Fang], Zhang, Y.C.[Yong-Chao], Yang, J.Y.[Jian-Yu], Huang, Y.L.[Yu-Lin],
A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
Target detection ability of marine navigation radars. BibRef

Uereyen, S.[Soner], Bachofer, F.[Felix], Kuenzer, C.[Claudia],
A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables-A Case Study for Indo-Gangetic River Basins,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Li, H.D.[Hao-Dong], Liao, G.S.[Gui-Sheng], Xu, J.W.[Jing-Wei], Lan, L.[Lan],
An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Xu, X.W.[Xiao-Wo], Zhang, X.L.[Xiao-Ling], Zhang, T.W.[Tian-Wen],
Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Liu, S.W.[Shan-Wei], Kong, W.M.[Wei-Min], Chen, X.F.[Xing-Feng], Xu, M.M.[Ming-Ming], Yasir, M.[Muhammad], Zhao, L.M.[Li-Min], Li, J.G.[Jia-Guo],
Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Zhu, M.M.[Ming-Ming], Hu, G.P.[Guo-Ping], Zhou, H.[Hao], Wang, S.Q.[Shi-Qiang], Feng, Z.[Ziang], Yue, S.J.[Shi-Jie],
A Ship Detection Method via Redesigned FCOS in Large-Scale SAR Images,
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Synthetic Aperture Radar (SAR), Scattering mechanism, Wave polarization anisotropy, Relatively weakly scattering targets BibRef

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Li, S.[Sen], Fu, X.J.[Xiong-Jun], Dong, J.[Jian],
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Rizaev, I.G.[Igor G.], Achim, A.[Alin],
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Li, Z.Y.[Zhen-Yu], Chen, J.L.[Jian-Lai], Xiong, Y.[Yi], Yu, H.[Hanwen], Zhang, H.[Huaigen], Gao, B.[Bing],
A Ship Detection and Imagery Scheme for Airborne Single-Channel SAR in Coastal Regions,
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Xu, Z.J.[Zhi-Jing], Gao, R.[Rui], Huang, K.[Kan], Xu, Q.H.[Qi-Hui],
Triangle Distance IoU Loss, Attention-Weighted Feature Pyramid Network, and Rotated-SARShip Dataset for Arbitrary-Oriented SAR Ship Detection,
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Guo, Y.[Yiyu], Zhou, L.[Luoyu],
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Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method,
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Wang, S.[Simin], Gao, S.[Song], Zhou, L.[Lun], Liu, R.C.[Ruo-Chen], Zhang, H.S.[Heng-Sheng], Liu, J.M.[Jia-Ming], Jia, Y.[Yong], Qian, J.[Jiang],
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Zhang, L.Y.[Lu-Yang], Wang, H.T.[Hai-Tao], Wang, L.F.[Ling-Feng], Pan, C.[Chunhong], Huo, C.L.[Chun-Lei], Liu, Q.[Qiang], Wang, X.[Xinyao],
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On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship,
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BoxPaste: An Effective Data Augmentation Method for SAR Ship Detection,
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Yao, C.[Canming], Xie, P.F.[Peng-Fei], Zhang, L.[Lei], Fang, Y.Y.[Yu-Yuan],
ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images,
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Su, L.Y.[Li-Yang], Kiang, J.F.[Jean-Fu],
Multi-Channel SAR Imaging on Cruising Ships with Sub-Orbital Spaceplane,
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Xiong, B.[Boli], Sun, Z.Z.[Zhong-Zhen], Wang, J.[Jin], Leng, X.G.[Xiang-Guang], Ji, K.[Kefeng],
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Lang, H.T.[Hai-Tao], Wang, R.[Ruifu], Zheng, S.Y.[Shao-Ying], Wu, S.[Siwen], Li, J.[Jialu],
Ship Classification in SAR Imagery by Shallow CNN Pre-Trained on Task-Specific Dataset with Feature Refinement,
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Lisowski, J.[Józef], Szklarski, A.[Andrzej],
Quality Analysis of Small Maritime Target Detection by Means of Passive Radar Reflectors in Different Sea States,
RS(14), No. 24, 2022, pp. xx-yy.
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Zhan, R.H.[Rong-Hui], Cui, Z.Y.[Zong-Yong],
Ship Recognition for SAR Scene Images under Imbalance Data,
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Sun, Z.[Zequn], Meng, C.[Chunning], Cheng, J.[Jierong], Zhang, Z.Q.[Zhi-Qing], Chang, S.J.[Sheng-Jiang],
A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images,
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Wang, J.L.[Jie-Lei], Cui, Z.Y.[Zong-Yong], Jiang, T.[Ting], Cao, C.J.[Chang-Jie], Cao, Z.J.[Zong-Jie],
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IP(32), 2023, pp. 565-579.
IEEE DOI 2301
Marine vehicles, Task analysis, Optimization, Object detection, Deep learning, Radar polarimetry, Neural networks, network pruning BibRef

Sun, W.F.[Wei-Feng], Li, X.T.[Xiao-Tong], Ji, Y.G.[Yong-Gang], Dai, Y.S.[Yong-Shou], Huang, W.M.[Wei-Min],
Plot Quality Aided Plot-to-Track Association in Dense Clutter for Compact High-Frequency Surface Wave Radar,
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Zhang, L.[Lili], Liu, Y.X.[Yu-Xuan], Qu, L.[Lele], Cai, J.[Jiannan], Fang, J.[Junpeng],
A Spatial Cross-Scale Attention Network and Global Average Accuracy Loss for SAR Ship Detection,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Zhou, Y.C.[Yu-Cheng], Fu, K.[Kun], Han, B.[Bing], Yang, J.X.[Jun-Xin], Pan, Z.X.[Zong-Xu], Hu, Y.X.[Yu-Xin], Yin, D.[Di],
D-MFPN: A Doppler Feature Matrix Fused with a Multilayer Feature Pyramid Network for SAR Ship Detection,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Shao, Z.[Zikang], Zhang, X.L.[Xiao-Ling], Wei, S.[Shunjun], Shi, J.[Jun], Ke, X.[Xiao], Xu, X.[Xiaowo], Zhan, X.[Xu], Zhang, T.W.[Tian-Wen], Zeng, T.J.[Tian-Jiao],
Scale in Scale for SAR Ship Instance Segmentation,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Jiang, X.Q.[Xin-Qiao], Xie, H.[Hongtu], Chen, J.X.[Jia-Xing], Zhang, J.[Jian], Wang, G.Q.[Guo-Qian], Xie, K.[Kai],
Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Zhang, Y.P.[Yi-Peng], Lu, D.D.[Dong-Dong], Qiu, X.L.[Xiao-Lan], Li, F.[Fei],
Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
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Alessi, M.A.[Marissa A.], Chirico, P.G.[Peter G.], Sunder, S.[Sindhuja], O'Pry, K.L.[Kelsey L.],
Detection and Monitoring of Small-Scale Diamond and Gold Mining Dredges Using Synthetic Aperture Radar on the Kadei (Sangha) River, Central African Republic,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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del Prete, R.[Roberto], Graziano, M.D.[Maria Daniela], Renga, A.[Alfredo],
Unified Framework for Ship Detection in Multi-Frequency SAR Images: A Demonstration with COSMO-SkyMed, Sentinel-1, and SAOCOM Data,
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Zhao, S.Y.[Si-Yuan], Luo, Y.[Ying], Zhang, T.[Tao], Guo, W.W.[Wei-Wei], Zhang, Z.H.[Zeng-Hui],
A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection,
PandRS(198), 2023, pp. 16-29.
Elsevier DOI 2304
Multisource satellite-borne synthetic aperture radar (SAR), Object detection, Domain adaptation (DA), Domain-specific knowledge BibRef

Qiu, W.X.[Wei-Xing], Pan, Z.X.[Zong-Xu], Yang, J.W.[Jian-Wei],
Few-Shot PolSAR Ship Detection Based on Polarimetric Features Selection and Improved Contrastive Self-Supervised Learning,
RS(15), No. 7, 2023, pp. 1874.
DOI Link 2304
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Lanz, P.[Peter], Marino, A.[Armando], Simpson, M.D.[Morgan David], Brinkhoff, T.[Thomas], Köster, F.[Frank], Möller, M.[Matthias],
The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR,
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Ge, J.Y.[Jun-Yao], Tang, Y.P.[Yi-Ping], Guo, K.T.[Kai-Tai], Zheng, Y.[Yang], Hu, H.[Haihong], Liang, J.[Jimin],
KeyShip: Towards High-Precision Oriented SAR Ship Detection Using Key Points,
RS(15), No. 8, 2023, pp. 2035.
DOI Link 2305
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Chen, Z.[Zhuo], Liu, C.[Chang], Filaretov, V.F., Yukhimets, D.A.,
Multi-Scale Ship Detection Algorithm Based on YOLOv7 for Complex Scene SAR Images,
RS(15), No. 8, 2023, pp. 2071.
DOI Link 2305
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Shao, Z.K.[Zi-Kang], Zhang, T.W.[Tian-Wen], Ke, X.[Xiao],
A Dual-Polarization Information-Guided Network for SAR Ship Classification,
RS(15), No. 8, 2023, pp. 2138.
DOI Link 2305
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Chen, P.[Peng], Zhou, H.[Hui], Li, Y.[Ying], Liu, P.[Peng], Liu, B.X.[Bing-Xin],
A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Jin, X.F.[Xin-Fei], Su, F.[Fulin], Li, H.X.[Hong-Xu], Xu, Z.[Zihan], Deng, J.[Jie],
Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Tang, G.[Gang], Zhao, H.[Hongren], Claramunt, C.[Christophe], Zhu, W.D.[Wei-Dong], Wang, S.M.[Shi-Ming], Wang, Y.[Yide], Ding, Y.[Yuehua],
PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images,
RS(15), No. 11, 2023, pp. 2855.
DOI Link 2306
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Zhu, H.[Hairui], Guo, S.[Shanhong], Sheng, W.X.[Wei-Xing], Xiao, L.[Lei],
SCM: A Searched Convolutional Metaformer for SAR Ship Classification,
RS(15), No. 11, 2023, pp. 2904.
DOI Link 2306
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Yu, N.J.[Nan-Jing], Ren, H.H.[Hao-Hao], Deng, T.M.[Tian-Min], Fan, X.B.[Xiao-Biao],
A Lightweight Radar Ship Detection Framework with Hybrid Attentions,
RS(15), No. 11, 2023, pp. 2743.
DOI Link 2306
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Wang, J.[Jin], Leng, X.G.[Xiang-Guang], Sun, Z.Z.[Zhong-Zhen], Zhang, X.[Xi], Ji, K.[Kefeng],
Refocusing Swing Ships in SAR Imagery Based on Spatial-Variant Defocusing Property,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Zhang, Y.M.[Yi-Min], Chen, C.[Chuxuan], Hu, R.[Ronglin], Yu, Y.T.[Yong-Tao],
ESarDet: An Efficient SAR Ship Detection Method Based on Context Information and Large Effective Receptive Field,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Liu, Q.Q.[Qian-Qian], Li, D.[Dong], Jiang, R.J.[Ren-Jie], Liu, S.[Shuang], Liu, H.Q.[Hong-Qing], Li, S.[Suqi],
MT-FANet: A Morphology and Topology-Based Feature Alignment Network for SAR Ship Rotation Detection,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Pan, X.[Xueli], Li, N.[Nana], Yang, L.X.[Li-Xia], Huang, Z.X.[Zhi-Xiang], Chen, J.[Jie], Wu, Z.H.[Zhen-Hua], Zheng, G.Q.[Guo-Qing],
Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images,
RS(15), No. 13, 2023, pp. 3258.
DOI Link 2307
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Pu, X.Y.[Xin-Yang], Jia, H.[Hecheng], Xin, Y.[Yu], Wang, F.[Feng], Wang, H.P.[Hai-Peng],
Ship Detection in Low-Quality SAR Images via an Unsupervised Domain Adaption Method,
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Li, R.[Ruimin], Li, J.[Jichao], Gou, S.[Shuiping], Lu, H.[Haofan], Mao, S.[Shasha], Guo, Z.[Zhang],
Multi-Scale Similarity Guidance Few-Shot Network for Ship Segmentation in SAR Images,
RS(15), No. 13, 2023, pp. 3304.
DOI Link 2307
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Bezerra, D.X.[Diego X.], Lorenzzetti, J.A.[Joăo A.], Paes, R.L.[Rafael L.],
Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images,
RS(15), No. 13, 2023, pp. 3441.
DOI Link 2307
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Ren, X.Z.[Xiao-Zhen], Bai, Y.[Yanwen], Liu, G.[Gang], Zhang, P.[Ping],
YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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Chen, Z.[Zhe], Ding, Z.Q.[Zhi-Quan], Zhang, X.L.[Xiao-Ling], Wang, X.T.[Xiao-Ting], Zhou, Y.Y.[Yuan-Yuan],
Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images,
RS(15), No. 15, 2023, pp. xx-yy.
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Gao, G.[Gui], Bai, Q.[Qilin], Zhang, C.[Chuan], Zhang, L.L.[Lin-Lin], Yao, L.[Libo],
Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection,
PandRS(202), 2023, pp. 663-681.
Elsevier DOI 2308
Polarimetric synthetic aperture radar, Dualistic cascade convolutional neural network, Ship detection BibRef

Xu, C.[Congan], Gao, L.[Long], Su, H.[Hang], Zhang, J.T.[Jian-Ting], Wu, J.F.[Jun-Feng], Yan, W.J.[Wen-Jun],
Label Smoothing Auxiliary Classifier Generative Adversarial Network with Triplet Loss for SAR Ship Classification,
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DOI Link 2309
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Xu, Z.J.[Zhi-Jing], Zhai, J.[Jinle], Huang, K.[Kan], Liu, K.[Kun],
DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection,
RS(15), No. 18, 2023, pp. 4546.
DOI Link 2310
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Zhang, Y.Y.[Yang-Yang], Xu, N.[Ning], Li, N.[Ning], Guo, Z.W.[Zheng-Wei],
A Multi-Domain Joint Novel Method for ISAR Imaging of Multi-Ship Targets,
RS(15), No. 19, 2023, pp. 4878.
DOI Link 2310
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Zhang, Y.[Yue], Jiang, S.[Shuai], Cao, Y.[Yue], Xiao, J.[Jiarong], Li, C.[Chengkun], Zhou, X.[Xuan], Yu, Z.J.[Zhong-Jun],
Hardware-Aware Design of Speed-Up Algorithms for Synthetic Aperture Radar Ship Target Detection Networks,
RS(15), No. 20, 2023, pp. 4995.
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Fang, L.[Lu], Yang, Z.Y.[Zi-Yuan], Mu, W.X.[Wen-Xing], Liu, T.[Tao],
A Novel Polarization Scattering Decomposition Model and Its Application to Ship Detection,
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Conditional diffusion models, Detection datasets generation, SAR ship detection BibRef


Yao, C.[Cheng], Bai, L.[Lin], Xue, D.L.[Dong-Ling], Lin, X.Y.[Xiang-Yuan], Ye, Z.[Zhen], Wang, Y.Q.[Yan-Qi], Yin, K.[Kangdi],
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ICIVC22(166-171)
IEEE DOI 2301
Location awareness, Radar detection, Detectors, Object detection, Radar imaging, Feature extraction, Radar polarimetry, synthetic aperture radar (SAR) BibRef

Zhang, H.[Hui], Liu, S.W.[Shen-Wen], Li, Y.Z.[Yong-Zhen], Chen, S.W.[Si-Wei],
Compact-Pol SAR Ship Detection Combining Sublook Analysis and Tucker Decomposition,
ICIVC22(208-213)
IEEE DOI 2301
Support vector machines, Time-frequency analysis, Tensors, Filtering, Feature extraction, Polarimetry, Data mining, ship detection BibRef

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ICIP22(2526-2530)
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Training, Location awareness, Image segmentation, Stochastic processes, Radar imaging, Predictive models, Synthetic aperture radar BibRef

Cheng, Y.W.[Yu-Wei], Xu, H.[Hu], Liu, Y.M.[Yi-Min],
Robust Small Object Detection on the Water Surface through Fusion of Camera and Millimeter Wave Radar,
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Point cloud compression, Surface waves, Radar detection, Object detection, Radar, Millimeter wave radar, Radar imaging, Vision + other modalities BibRef

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CVIDL20(560-565)
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convolutional neural nets, learning (artificial intelligence), object detection, radar computing, radar detection, radar imaging, SAR-Ship-Dataset BibRef

Li, Z.[Zhi], Qu, C.W.[Chang-Wen], Zhou, Q.[Qiang], Liu, C.[Chen], Peng, S.J.[Shu-Juan], Li, J.W.[Jian-Wei],
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ICIVC17(13-19)
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Clutter, Fitting, Image segmentation, Marine vehicles, Mathematical model, Strips, constant false alarm rate, ship detection, the SAR image, and-sea segmentation, port area, sea clutter model BibRef

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ICIP15(4426-4430)
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CFAR; Synthetic aperture radar; clutter modeling; ship detection BibRef

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An Improved Automatic Ship Detection Method in SAR Images,
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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Ship Wake Detection .


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