22.9.4.2.2 Radar, SAR, Ship Detection

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.[Zijun],
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.[Hongmiao], 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

Ma, H., Antoniou, M., Stove, A.G., Winkel, J., Cherniakov, M.,
Maritime Moving Target Localization Using Passive GNSS-Based Multistatic Radar,
GeoRS(56), No. 8, August 2018, pp. 4808-4819.
IEEE DOI 1808
passive radar, satellite navigation, target tracking, maritime moving target localization, target localization 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.[Weibo], 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.[Tianwen], 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.[Donghan], 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], Lefèvre, 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
BibRef

Liu, G.[Genwang], Zhang, X.[Xi], Meng, J.[Junmin],
A Small Ship Target Detection Method Based on Polarimetric SAR,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Liang, Y.[Yi], Sun, K.[Kun], Zeng, Y.[Yugui], Li, G.[Guofei], 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
BibRef

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
BibRef

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

Zhang, L.[Ling], Mao, D.W.[Dong-Wei], Niu, J.[Jiong], Wu, Q.M.J.[Q. M. Jonathan], Ji, Y.G.[Yong-Gang],
Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
High frequency surface wave radar. marine surveillance. 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
BibRef

Sun, W.F.[Wei-Feng], Ji, M.J.[Meng-Jie], Huang, W.M.[Wei-Min], Ji, Y.G.[Yong-Gang], Dai, Y.[Yongshou],
Vessel Tracking Using Bistatic Compact HFSWR,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

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.[Xuesong],
Ship Recognition from Chaff Clouds with Sophisticated Polarimetric Decomposition,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

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.
DOI Link 2006
BibRef

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.
DOI Link 2007
BibRef

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,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Shi, J.[Jun], Wei, S.[Shunjun],
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

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., Li, G., Zhang, X., He, Y.,
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

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
BibRef

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
BibRef

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

Lanz, P.[Peter], Marino, A.[Armando], Brinkhoff, T.[Thomas], Köster, F.[Frank], Möller, M.[Matthias],
The InflateSAR Campaign: Evaluating SAR Identification Capabilities of Distressed Refugee Boats,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
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
BibRef

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.[Xinbo],
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

Chen, Y., Yu, J., Xu, Y.,
SAR Ship Target Detection for SSDv2 under Complex Backgrounds,
CVIDL20(560-565)
IEEE DOI 2102
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],
Ship detection in harbor area in SAR images based on constructing an accurate sea-clutter model,
ICIVC17(13-19)
IEEE DOI 1708
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

Negula, I.D.[I. Dana], Poenaru, V.D., Olteanu, V.G., Badea, A.,
Sentinel-1/2 Data For Ship Traffic Monitoring On The Danube River,
ISPRS16(B8: 37-41).
DOI Link 1610
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Martin-de-Nicolas, J., Jarabo-Amores, P., Rey-Maestre, N., Mata-Moya, D., Barcena-Humanes, J.L.,
A non-parametric CFAR detector based on SAR sea clutter statistical modeling,
ICIP15(4426-4430)
IEEE DOI 1512
CFAR; Synthetic aperture radar; clutter modeling; ship detection BibRef

Chen, W.T.[Wen-Ting], Ji, K.F.[Ke-Feng], Xing, X.W.[Xiang-Wei], Zou, H.X.[Huan-Xin], Sun, H.[Hao],
Ship recognition in high resolution SAR imagery based on feature selection,
CVRS12(301-305).
IEEE DOI 1302
BibRef

Zhuo, C.[Chen],
An Improved Automatic Ship Detection Method in SAR Images,
CISP09(1-4).
IEEE DOI 0910
BibRef

Li, W.B.[Wei-Bin], He, M.Y.[Ming-Yi], Zhang, S.L.[Shun-Li],
A Heterogeneity-Based Ship Detection Algorithm for SAR Imagery,
CISP09(1-5).
IEEE DOI 0910
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Gagnon, L., Klepko, R.,
Hierarchical Classifier Design for Airborne SAR Images of Ships,
SPIE(3371), 1998, pp. xx
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Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
ATR -- IR, Infra-Red, Thermal, Applications .

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