24.8.4.4.2 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.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.C.[Xi-Chao], 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], 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.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
BibRef

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
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

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

Li, Y.Z.[Yong-Zhen], Quan, S.N.[Si-Nong], 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.
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.C.[Xi-Chao], 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.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,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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.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
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

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.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
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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
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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
BibRef

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
BibRef

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,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Guo, G.B.[Guang-Bin], Guo, L.X.[Li-Xin], Wang, R.[Rui], Liu, W.[Wei], Li, L.[Long],
Transient Scattering Echo Simulation and ISAR Imaging for a Composite Target-Ocean Scene Based on the TDSBR Method,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
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Xia, R.F.[Run-Fan], Chen, J.[Jie], Huang, Z.X.[Zhi-Xiang], Wan, H.Y.[Hui-Yao], Wu, B.[Bocai], Sun, L.[Long], Yao, B.D.[Bai-Dong], Xiang, H.B.[Hai-Bing], Xing, M.D.[Meng-Dao],
CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
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Feng, Y.[Yun], Chen, J.[Jie], Huang, Z.X.[Zhi-Xiang], Wan, H.[Huiyao], Xia, R.F.[Run-Fan], Wu, B.[Bocai], Sun, L.[Long], Xing, M.D.[Meng-Dao],
A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
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Lv, J.M.[Jian-Ming], Chen, J.[Jie], Huang, Z.X.[Zhi-Xiang], Wan, H.Y.[Hui-Yao], Zhou, C.Y.[Chun-Yan], Wang, D.Y.[Dao-Yuan], Wu, B.[Bocai], Sun, L.[Long],
An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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He, B.[Bokun], Zhang, Q.Y.[Qing-Yi], Tong, M.[Ming], He, C.[Chu],
An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Sun, W.F.[Wei-Feng], Pang, Z.Z.[Zhen-Zhen], Huang, W.M.[Wei-Min], Ma, P.[Peng], Ji, Y.G.[Yong-Gang], Dai, Y.[Yongshou], Li, X.T.[Xiao-Tong],
A Multi-Stage Vessel Tracklet Association Method for Compact High-Frequency Surface Wave Radar,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Shi, H.[Hao], Fang, Z.H.[Zhong-Hao], Wang, Y.[Yupei], Chen, L.[Liang],
An Adaptive Sample Assignment Strategy Based on Feature Enhancement for Ship Detection in SAR Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Heiselberg, P.[Peder], Sørensen, K.A.[Kristian A.], Heiselberg, H.[Henning], Andersen, O.B.[Ole B.],
SAR Ship-Iceberg Discrimination in Arctic Conditions Using Deep Learning,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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He, B.[Bokun], Zhang, Q.Y.[Qing-Yi], Tong, M.[Ming], He, C.[Chu],
Oriented Ship Detector for Remote Sensing Imagery Based on Pairwise Branch Detection Head and SAR Feature Enhancement,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Ke, X.[Xiao], Zhang, X.L.[Xiao-Ling], Zhang, T.W.[Tian-Wen],
GCBANet: A Global Context Boundary-Aware Network for SAR Ship Instance Segmentation,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Zhu, M.M.[Ming-Ming], Hu, G.P.[Guo-Ping], Li, S.[Shuai], Zhou, H.[Hao], Wang, S.Q.[Shi-Qiang], Feng, Z.[Ziang],
A Novel Anchor-Free Method Based on FCOS + ATSS for Ship Detection in SAR Images,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Zhang, L.[Liang], Zhang, Z.J.[Zhi-Jun], Lu, S.T.[Sheng-Tao], Xiang, D.L.[De-Liang], Su, Y.[Yi],
Fast Superpixel-Based Non-Window CFAR Ship Detector for SAR Imagery,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling],
HTC+ for SAR Ship Instance Segmentation,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
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Li, J.W.[Jian-Wei], Xu, C.[Congan], Su, H.[Hang], Gao, L.[Long], Wang, T.[Taoyang],
Deep Learning for SAR Ship Detection: Past, Present and Future,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
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Gu, M.F.[Ming-Fei], Wang, Y.H.[Ying-Hua], Liu, H.W.[Hong-Wei], Wang, P.H.[Peng-Hui],
PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Cheng, J.[Jianda], Xiang, D.L.[De-Liang], Tang, J.X.[Jia-Xin], Zheng, Y.P.[Yan-Peng], Guan, D.D.[Dong-Dong], Du, B.[Bin],
Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Raj, J.A.[J. Anil], Idicula, S.M.[Sumam Mary], Paul, B.[Binu],
Ship identification from SAR image using novel deep learning method with reduced false prediction,
IJCVR(12), No. 4, 2022, pp. 411-425.
DOI Link 2207
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Zhang, C.[Chuan], Gao, G.[Gui], Zhang, L.L.[Lin-Lin], Chen, C., Gao, S., Yao, L.[Libo], Bai, Q.[Qilin], Gou, S.[Shiquan],
A novel full-polarization SAR image ship detector based on scattering mechanisms and wave polarization anisotropy,
PandRS(190), 2022, pp. 129-143.
Elsevier DOI 2208
Synthetic Aperture Radar (SAR), Scattering mechanism, Wave polarization anisotropy, Relatively weakly scattering targets BibRef

Ustalli, N.[Nertjana], Villano, M.[Michelangelo],
High-Resolution Wide-Swath Ambiguous Synthetic Aperture Radar Modes for Ship Monitoring,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
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Yu, W.B.[Wen-Bo], Wang, Z.J.[Zi-Jian], Li, J.[Jiamu], Luo, Y.H.[Yun-Hua], Yu, Z.J.[Zhong-Jun],
A Lightweight Network Based on One-Level Feature for Ship Detection in SAR Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Shao, Z.K.[Zi-Kang], Zhang, X.L.[Xiao-Ling], Zhang, T.W.[Tian-Wen], Xu, X.W.[Xiao-Wo], Zeng, T.J.[Tian-Jiao],
RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Shu, G.F.[Gao-Feng], Chang, J.[Jiahui], Lu, J.[Jing], Wang, Q.[Qing], Li, N.[Ning],
A Novel Method for SAR Ship Detection Based on Eigensubspace Projection,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Yu, J.[Jimin], Wu, T.[Tao], Zhou, S.[Shangbo], Pan, H.[Huilan], Zhang, X.[Xin], Zhang, W.[Wei],
An SAR Ship Object Detection Algorithm Based on Feature Information Efficient Representation Network,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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Li, X.Q.[Xiu-Qin], Li, D.[Dong], Liu, H.Q.[Hong-Qing], Wan, J.[Jun], Chen, Z.[Zhanye], Liu, Q.H.[Qing-Hua],
A-BFPN: An Attention-Guided Balanced Feature Pyramid Network for SAR Ship Detection,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Li, S.[Sen], Fu, X.J.[Xiong-Jun], Dong, J.[Jian],
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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],
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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],
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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],
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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.
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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,
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Elsevier DOI 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],
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Chen, Z.[Zhuo], Liu, C.[Chang], Filaretov, V.F., Yukhimets, D.A.,
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Shao, Z.K.[Zi-Kang], Zhang, T.W.[Tian-Wen], Ke, X.[Xiao],
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Chen, P.[Peng], Zhou, H.[Hui], Li, Y.[Ying], Liu, P.[Peng], Liu, B.X.[Bing-Xin],
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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,
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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,
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Zhu, H.[Hairui], Guo, S.[Shanhong], Sheng, W.X.[Wei-Xing], Xiao, L.[Lei],
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Yu, N.J.[Nan-Jing], Ren, H.H.[Hao-Hao], Deng, T.M.[Tian-Min], Fan, X.B.[Xiao-Biao],
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Wang, J.[Jin], Leng, X.G.[Xiang-Guang], Sun, Z.Z.[Zhong-Zhen], Zhang, X.[Xi], Ji, K.[Kefeng],
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DOI Link 2307
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Zhang, Y.M.[Yi-Min], Chen, C.X.[Chu-Xuan], Hu, R.L.[Rong-Lin], Yu, Y.T.[Yong-Tao],
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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],
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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],
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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],
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Li, R.M.[Rui-Min], Li, J.[Jichao], Gou, S.P.[Shui-Ping], Lu, H.F.[Hao-Fan], Mao, S.[Shasha], Guo, Z.[Zhang],
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Bezerra, D.X.[Diego X.], Lorenzzetti, J.A.[João A.], Paes, R.L.[Rafael L.],
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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,
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Gao, G.[Gui], Bai, Q.[Qilin], Zhang, C.[Chuan], Zhang, L.L.[Lin-Lin], Yao, L.[Libo],
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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],
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Xu, Z.J.[Zhi-Jing], Zhai, J.[Jinle], Huang, K.[Kan], Liu, K.[Kun],
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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,
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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.
DOI Link 2310
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Huang, S.Q.[Shi-Qi], Zhang, O.[Ouya], Chen, Q.L.[Qi-Long],
Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering,
RS(15), No. 20, 2023, pp. 4972.
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Jiang, X.[Xinqiao], Xie, H.[Hongtu], Lu, Z.[Zheng], Hu, J.[Jun],
Energy-Efficient and High-Performance Ship Classification Strategy Based on Siamese Spiking Neural Network in Dual-Polarized SAR Images,
RS(15), No. 20, 2023, pp. 4966.
DOI Link 2310
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Liu, Y.[Yuan], Zhao, S.J.[Sheng-Jie], Han, F.X.[Feng-Xia], Chai, M.Q.[Meng-Qiu], Jiang, H.[Hao], Zhang, H.M.[Hong-Ming],
Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks,
RS(15), No. 21, 2023, pp. 5126.
DOI Link 2311
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Jafari, Z.[Zahra], Karami, E.[Ebrahim], Taylor, R.[Rocky], Bobby, P.[Pradeep],
Enhanced Ship/Iceberg Classification in SAR Images Using Feature Extraction and the Fusion of Machine Learning Algorithms,
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DOI Link 2311
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Feng, K.Y.[Kun-Yu], Lun, L.[Li], Wang, X.F.[Xiao-Feng], Cui, X.X.[Xiao-Xin],
LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion,
RS(15), No. 22, 2023, pp. 5309.
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Qiu, W.X.[Wei-Xing], Pan, Z.X.[Zong-Xu],
Polarimetric Synthetic Aperture Radar Ship Potential Area Extraction Based on Neighborhood Semantic Differences of the Latent Dirichlet Allocation Bag-of-Words Topic Model,
RS(15), No. 23, 2023, pp. 5601.
DOI Link 2312
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Zhang, L.[Lizun], Zhou, H.[Hao], Bai, L.Y.[Li-Yun], Tian, Y.W.[Ying-Wei],
A Grid-Based Gradient Descent Extended Target Clustering Method and Ship Target Inverse Synthetic Aperture Radar Imaging for UHF Radar,
RS(15), No. 23, 2023, pp. 5466.
DOI Link 2312
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Lu, Z.[Zhuhao], Wang, P.F.[Peng-Fei], Li, Y.J.[Ya-Jun], Ding, B.G.[Bao-Gang],
A New Deep Neural Network Based on SwinT-FRM-ShipNet for SAR Ship Detection in Complex Near-Shore and Offshore Environments,
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DOI Link 2401
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Alessi, M.A.[Marissa A.], Chirico, P.G.[Peter G.], Millones, M.[Marco],
Artisanal Mining River Dredge Detection Using SAR: A Method Comparison,
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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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Zhang, X.[Xin], Li, Y.[Yang], Li, F.[Feng], Jiang, H.Z.[Hang-Zhi], Wang, Y.H.[Yan-Hua], Zhang, L.[Liang], Zheng, L.[Le], Ding, Z.[Zegang],
Ship-Go: SAR Ship Images Inpainting via instance-to-image Generative Diffusion Models,
PandRS(207), 2024, pp. 203-217.
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Conditional diffusion models, Detection datasets generation, SAR ship detection BibRef

Chen, B.J.[Bing-Ji], Xue, F.[Fengli], Song, H.J.[Hong-Jun],
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Yang, Y.R.[Yan-Rui], Chen, J.[Jie], Sun, L.[Long], Zhou, Z.[Zheng], Huang, Z.X.[Zhi-Xiang], Wu, B.[Bocai],
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Xiao, D.Z.[De-Zhu], Zhang, X.[Xin], Yang, Q.[Qiang], Li, J.M.[Jia-Ming],
Improved Main Lobe Cancellation Method for Suppression Directional Noise in HFSWR Systems,
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For vessel detection. BibRef

Ji, Y.Z.[Yuan-Zheng], Liu, A.[Aijun], Chen, X.[Xuekun], Wang, J.Q.[Jia-Qi], Yu, C.J.[Chang-Jun],
Target Detection Method for High-Frequency Surface Wave Radar RD Spectrum Based on (VI)CFAR-CNN and Dual-Detection Maps Fusion Compensation,
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Liu, M.Q.[Min-Qin], Zhu, B.[Bo], Ma, H.B.[Hong-Bing],
A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments,
RS(16), No. 4, 2024, pp. 664.
DOI Link 2402
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Yang, Z.G.[Zhi-Gang], Xia, X.Y.[Xiang-Yu], Liu, Y.M.[Yi-Ming], Wen, G.[Guiwei], Zhang, W.E.[Wei Emma], Guo, L.M.[Li-Min],
LPST-Det: Local-Perception-Enhanced Swin Transformer for SAR Ship Detection,
RS(16), No. 3, 2024, pp. 483.
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Tang, H.[Hongdou], Gao, S.[Song], Li, S.[Song], Wang, P.Y.[Peng-Yu], Liu, J.Q.[Ji-Qiu], Wang, S.[Simin], Qian, J.[Jiang],
A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7,
RS(16), No. 3, 2024, pp. 486.
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Wang, J.Q.[Jia-Qi], Liu, A.[Aijun], Yu, C.J.[Chang-Jun], Ji, Y.Z.[Yuan-Zheng],
Ship Formation Identification with Spatial Features and Deep Learning for HFSWR,
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Zhang, H.[Handan], Wu, Y.[Yiquan],
CSEF-Net: Cross-Scale SAR Ship Detection Network Based on Efficient Receptive Field and Enhanced Hierarchical Fusion,
RS(16), No. 4, 2024, pp. 622.
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Yu, H.L.[Heng-Li], Ding, H.[Hao], Cao, Z.[Zheng], Liu, N.B.[Ning-Bo], Wang, G.Q.[Guo-Qing], Zhang, Z.X.[Zhao-Xiang],
A Floating Small Target Identification Method Based on Doppler Time Series Information,
RS(16), No. 3, 2024, pp. 505.
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Liu, Y.L.[Yi-Lin], Ma, Y.[Yong], Chen, F.[Fu], Shang, E.[Erping], Yao, W.[Wutao], Zhang, S.Y.[Shu-Yan], Yang, J.[Jin],
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Wen, X.[Xue], Zhang, S.[Shaoming], Wang, J.[Jianmei], Yao, T.[Tangjun], Tang, Y.[Yan],
A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s,
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Wu, K.[Kun], Zhang, Z.J.[Zhi-Jian], Chen, Z.[Zeyu], Liu, G.H.[Guo-Hua],
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RS(16), No. 6, 2024, pp. 1001.
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Pan, D.[Dece], Wu, Y.[Youming], Dai, W.[Wei], Miao, T.[Tian], Zhao, W.C.[Wen-Chao], Gao, X.[Xin], Sun, X.[Xian],
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Zhou, Y.[Yun], Wang, S.S.[Sen-Sen], Ren, H.H.[Hao-Hao], Hu, J.Y.[Jun-Yi], Zou, L.[Lin], Wang, X.G.[Xue-Gang],
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DOI Link 2403
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Wu, S.J.[Shu-Jie], Wang, W.[Wei], Deng, J.[Jie], Quan, S.N.[Si-Nong], Ruan, F.[Feng], Guo, P.C.[Peng-Cheng], Fan, H.Q.[Hong-Qi],
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DOI Link 2403
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IEEE DOI 2405
Training, Synthetic aperture radar, Search problems, Marine vehicles, Task analysis, Radar polarimetry, zero-shot neural architecture search (NAS) BibRef

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Elsevier DOI 2406
Ship detection, YOLO-Ships, Feature enhancement, Lightweight network, Attention mechanism BibRef

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IEEE DOI 2407
Marine vehicles, Radar, Radar imaging, Feature extraction, Object recognition, Radar tracking, Clutter, Marine radar, maritime management BibRef

Zhang, X.Z.[Xin-Zheng], Li, J.L.[Jin-Lin], Li, C.[Chao], Liu, G.J.[Guo-Jin],
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DOI Link 2408
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Yang, Y.M.[Yi-Min], Lang, P.[Ping], Yin, J.J.[Jun-Jun], He, Y.[Yaomin], Yang, J.[Jian],
Data Matters: Rethinking the Data Distribution in Semi-Supervised Oriented SAR Ship Detection,
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Wu, B.[Baolong], Wang, H.[Haonan], Zhang, C.[Cunle], Chen, J.[Jianlai],
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RS(16), No. 16, 2024, pp. 3001.
DOI Link 2408
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Chen, D.D.[Dong-Dong], Ju, R.S.[Ru-Sheng], Tu, C.Y.[Chuang-Ye], Long, G.W.[Guang-Wei], Liu, X.Y.[Xiao-Yang], Liu, J.Y.[Ji-Yuan],
GDB-YOLOv5s: Improved YOLO-based model for ship detection in SAR images,
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DOI Link 2409
computer vision, convolutional neural nets, feature extraction, image recognition, object detection, ships, synthetic aperture radar BibRef

Li, J.W.[Jian-Wei], Yu, Z.T.[Zhen-Tao], Chen, J.[Jie], Chi, C.[Cheng], Yu, L.[Lu], Cheng, P.[Pu],
Improve the Performance of SAR Ship Detectors by Small Object Detection Strategies,
RS(16), No. 17, 2024, pp. 3338.
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Tan, X.D.[Xiang-Dong], Leng, X.G.[Xiang-Guang], Sun, Z.Z.[Zhong-Zhen], Luo, R.[Ru], Ji, K.[Kefeng], Kuang, G.Y.[Gang-Yao],
Lightweight Ship Detection Network for SAR Range-Compressed Domain,
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Meng, F.L.[Fan-Long], Qi, X.Y.[Xiang-Yang], Fan, H.T.[Huai-Tao],
LSR-Det: A Lightweight Detector for Ship Detection in SAR Images Based on Oriented Bounding Box,
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Wang, C.Y.[Chun-Yuan], Cai, X.J.[Xian-Jun], Wu, F.[Fei], Cui, P.[Peng], Wu, Y.[Yang], Zhang, Y.[Ye],
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A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning,
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DOI Link 2410
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CPINet: Towards A Novel Cross-Polarimetric Interaction Network for Dual-Polarized SAR Ship Classification,
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DOI Link 2410
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High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar,
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Deep Hybrid Fusion Network for Inverse Synthetic Aperture Radar Ship Target Recognition Using Multi-Domain High-Resolution Range Profile Data,
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Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks,
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DOI Link 2411
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Cai, P.X.[Pei-Xin], Liu, B.X.[Bing-Xin], Wang, P.L.[Pei-Lin], Liu, P.[Peng], Yuan, Y.[Yu], Li, X.H.[Xin-Hao], Chen, P.[Peng], Li, Y.[Ying],
SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection,
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A Novel Detection Transformer Framework for Ship Detection in Synthetic Aperture Radar Imagery Using Advanced Feature Fusion and Polarimetric Techniques,
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SAR for identifying vessels engaged in illegal fishing. Deep learning, Training, Target tracking, Surveillance, Object detection, Radar polarimetry 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],
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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)
IEEE DOI 2211
Training, Location awareness, Image segmentation, Stochastic processes, Radar imaging, Predictive models, Synthetic aperture radar BibRef

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Robust Small Object Detection on the Water Surface through Fusion of Camera and Millimeter Wave Radar,
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IEEE DOI 2203
Point cloud compression, Surface waves, Radar detection, Object detection, Radar, Millimeter wave radar, Radar imaging, Vision + other modalities BibRef

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

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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

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CFAR; Synthetic aperture radar; clutter modeling; ship detection BibRef

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