24.8.4.4.3 Radar, SAR, Ship 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., 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

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

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

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

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

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

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

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

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

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

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

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

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.B.[Ying-Bo], 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

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, 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

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

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

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

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

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

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

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

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

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

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, 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
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
BibRef

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
BibRef

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
BibRef

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
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

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

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

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, 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.L.[Ling-Li], 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

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

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],
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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],
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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.H.[Yue-Hua],
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Pan, X.L.[Xue-Li], 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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Li, R.M.[Rui-Min], Li, J.C.[Ji-Chao], Gou, S.P.[Shui-Ping], Lu, H.F.[Hao-Fan], Mao, S.[Shasha], Guo, Z.[Zhang],
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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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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],
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Zhang, Y.[Yue], Jiang, S.[Shuai], Cao, Y.[Yue], Xiao, J.R.[Jia-Rong], Li, C.K.[Cheng-Kun], Zhou, X.[Xuan], Yu, Z.J.[Zhong-Jun],
Hardware-Aware Design of Speed-Up Algorithms for Synthetic Aperture Radar Ship Target Detection Networks,
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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,
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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,
RS(15), No. 21, 2023, pp. 5202.
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.
DOI Link 2311
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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,
RS(15), No. 24, 2023, pp. 5780.
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,
RS(15), No. 24, 2023, pp. 5701.
DOI Link 2401
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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.
Elsevier DOI Code:
WWW Link. 2401
Conditional diffusion models, Detection datasets generation, SAR ship detection BibRef

Chen, B.J.[Bing-Ji], Xue, F.L.[Feng-Li], Song, H.J.[Hong-Jun],
A Lightweight Arbitrarily Oriented Detector Based on Transformers and Deformable Features for Ship Detection in SAR Images,
RS(16), No. 2, 2024, pp. 237.
DOI Link 2402
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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],
Unsupervised Domain-Adaptive SAR Ship Detection Based on Cross-Domain Feature Interaction and Data Contribution Balance,
RS(16), No. 2, 2024, pp. 420.
DOI Link 2402
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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,
RS(16), No. 2, 2024, pp. 254.
DOI Link 2402
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,
RS(16), No. 2, 2024, pp. 332.
DOI Link 2402
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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
BibRef

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.
DOI Link 2402
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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.
DOI Link 2402
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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,
RS(16), No. 3, 2024, pp. 577.
DOI Link 2402
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Zhang, H.[Handan], Wu, Y.Q.[Yi-Quan],
CSEF-Net: Cross-Scale SAR Ship Detection Network Based on Efficient Receptive Field and Enhanced Hierarchical Fusion,
RS(16), No. 4, 2024, pp. 622.
DOI Link 2402
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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.
DOI Link 2402
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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],
YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm,
RS(16), No. 5, 2024, pp. 913.
DOI Link 2403
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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,
RS(16), No. 5, 2024, pp. 733.
DOI Link 2403
BibRef

Wu, K.[Kun], Zhang, Z.J.[Zhi-Jian], Chen, Z.[Zeyu], Liu, G.H.[Guo-Hua],
Object-Enhanced YOLO Networks for Synthetic Aperture Radar Ship Detection,
RS(16), No. 6, 2024, pp. 1001.
DOI Link 2403
BibRef

Pan, D.[Dece], Wu, Y.M.[You-Ming], Dai, W.[Wei], Miao, T.[Tian], Zhao, W.C.[Wen-Chao], Gao, X.[Xin], Sun, X.[Xian],
TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images,
RS(16), No. 6, 2024, pp. 944.
DOI Link 2403
BibRef

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],
Multi-Level Feature-Refinement Anchor-Free Framework with Consistent Label-Assignment Mechanism for Ship Detection in SAR Imagery,
RS(16), No. 6, 2024, pp. 975.
DOI Link 2403
BibRef

Wang, L.[Lu], Qi, Y.H.[Yu-Hang], Mathiopoulos, P.T.[P. Takis], Zhao, C.H.[Chun-Hui], Mazhar, S.[Suleman],
An Improved SAR Ship Classification Method Using Text-to-Image Generation-Based Data Augmentation and Squeeze and Excitation,
RS(16), No. 7, 2024, pp. 1299.
DOI Link 2404
BibRef

Wu, F.[Falin], Hu, T.Y.[Tian-Yang], Xia, Y.[Yu], Ma, B.[Boyi], Sarwar, S.[Saddam], Zhang, C.X.[Chun-Xiao],
WDFA-YOLOX: A Wavelet-Driven and Feature-Enhanced Attention YOLOX Network for Ship Detection in SAR Images,
RS(16), No. 10, 2024, pp. 1760.
DOI Link 2405
BibRef

Zhang, Y.[Yu], Chen, W.H.[Wen-Hui], Li, S.L.[Song-Lin], Liu, H.L.[Hai-Long], Hu, Q.[Qing],
YOLO-Ships: Lightweight ship object detection based on feature enhancement,
JVCIR(101), 2024, pp. 104170.
Elsevier DOI 2406
Ship detection, YOLO-Ships, Feature enhancement, Lightweight network, Attention mechanism BibRef

Deng, J.[Jie], Su, F.[Fulin],
SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision,
RS(16), No. 11, 2024, pp. 1920.
DOI Link 2406
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Wang, X.[Xi], Xu, W.[Wei], Huang, P.P.[Ping-Ping], Tan, W.X.[Wei-Xian],
MSSD-Net: Multi-Scale SAR Ship Detection Network,
RS(16), No. 12, 2024, pp. 2233.
DOI Link 2406
BibRef

Ma, F.[Feng], Kang, Z.[Zhe], Chen, C.[Chen], Sun, J.[Jie], Xu, X.B.[Xiao-Bin], Wang, J.[Jin],
Identifying Ships From Radar Blips Like Humans Using a Customized Neural Network,
ITS(25), No. 7, July 2024, pp. 7187-7205.
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],
Multi-Teacher D-S Fusion for Semi-Supervised SAR Ship Detection,
RS(16), No. 15, 2024, pp. 2759.
DOI Link 2408
BibRef

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,
RS(16), No. 14, 2024, pp. 2551.
DOI Link 2408
BibRef

Wu, B.[Baolong], Wang, H.N.[Hao-Nan], Zhang, C.[Cunle], Chen, J.[Jianlai],
Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images,
RS(16), No. 16, 2024, pp. 3001.
DOI Link 2408
BibRef

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,
IET-IPR(18), No. 11, 2024, pp. 2869-2883.
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.
DOI Link 2409
BibRef

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,
RS(16), No. 17, 2024, pp. 3284.
DOI Link 2409
BibRef

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,
RS(16), No. 17, 2024, pp. 3251.
DOI Link 2409
BibRef

Wang, C.Y.[Chun-Yuan], Cai, X.J.[Xian-Jun], Wu, F.[Fei], Cui, P.[Peng], Wu, Y.[Yang], Zhang, Y.[Ye],
Stepwise Attention-Guided Multiscale Fusion Network for Lightweight and High-Accurate SAR Ship Detection,
RS(16), No. 17, 2024, pp. 3137.
DOI Link 2409
BibRef

Chan, H.P.[Hao-Peng], Qiu, X.L.[Xiao-Lan], Gao, X.[Xin], Lu, D.D.[Dong-Dong],
A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning,
RS(16), No. 18, 2024, pp. 3492.
DOI Link 2410
BibRef

Golubovic, D.[Dragan], Eric, M.[Miljko], Vukmirovis, N.[Nenad], Orlic, V.[Vladimir],
High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar,
RS(16), No. 18, 2024, pp. 3476.
DOI Link 2410
BibRef

Deng, J.[Jie], Su, F.[Fulin],
Deep Hybrid Fusion Network for Inverse Synthetic Aperture Radar Ship Target Recognition Using Multi-Domain High-Resolution Range Profile Data,
RS(16), No. 19, 2024, pp. 3701.
DOI Link 2410
BibRef

Wei, H.[Hang], Wang, Z.L.[Zu-Lin], Ni, Y.H.[Yuan-Han],
Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks,
RS(16), No. 21, 2024, pp. 4042.
DOI Link 2411
BibRef

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,
RS(16), No. 21, 2024, pp. 3952.
DOI Link 2411
BibRef

Qu, Y.M.[You-Min], Mao, X.P.[Xing-Peng], Hou, Y.G.[Yu-Guan], Li, X.[Xue],
An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR,
RS(16), No. 21, 2024, pp. 3929.
DOI Link 2411
High-frequency surface wave radar. Sea surface targets. BibRef

Cao, Q.[Qi], Chen, H.[Hang], Wang, S.[Shang], Wang, Y.Q.[Yong-Qiang], Fu, H.[Haisheng], Chen, Z.J.[Zhen-Jiao], Liang, F.[Feng],
LH-YOLO: A Lightweight and High-Precision SAR Ship Detection Model Based on the Improved YOLOv8n,
RS(16), No. 22, 2024, pp. 4340.
DOI Link 2412
BibRef

Tang, Y.S.[Yun-Shan], Zhang, Y.[Yue], Xiao, J.R.[Jia-Rong], Cao, Y.[Yue], Yu, Z.J.[Zhong-Jun],
An Enhanced Shuffle Attention with Context Decoupling Head with Wise IoU Loss for SAR Ship Detection,
RS(16), No. 22, 2024, pp. 4128.
DOI Link 2412
BibRef

Zhang, W.[Wandong], Yang, Y.M.[Yi-Min], Liu, T.L.[Tian-Long],
Coarse-to-Fine Target Detection for HFSWR With Spatial-Frequency Analysis and Subnet Structure,
MultMed(26), 2024, pp. 11290-11301.
IEEE DOI 2412
Marine vehicles, Object detection, Clutter, Neural networks, Signal processing algorithms, Heuristic algorithms, hierarchical neural network BibRef

Huang, Y.Y.[Yi-Yang], Wang, D.[Di], Wu, B.[Boxuan], An, D.[Daoxiang],
NST-YOLO11: ViT Merged Model with Neuron Attention for Arbitrary-Oriented Ship Detection in SAR Images,
RS(16), No. 24, 2024, pp. 4760.
DOI Link 2501
BibRef

Dai, D.[Dahai], Wu, H.[Hao], Wang, Y.[Yue], Ji, P.H.[Peng-Hui],
LHSDNet: A Lightweight and High-Accuracy SAR Ship Object Detection Algorithm,
RS(16), No. 23, 2024, pp. 4527.
DOI Link 2501
BibRef

Wang, K.Q.[Kai-Qi], Wang, Z.[Zeyu],
A Triple-Channel Network for Maritime Radar Targets Detection Based on Multi-Modal Features,
RS(16), No. 24, 2024, pp. 4662.
DOI Link 2501
BibRef

Hinai, A.A.A.[Al Adil Al], Guida, R.[Raffaella],
Confidence-Aware Ship Classification Using Contour Features in SAR Images,
RS(17), No. 1, 2025, pp. 127.
DOI Link 2501
BibRef

Zhang, M.J.[Ming-Jin], Li, Y.F.[Yao-Fei], Guo, J.[Jie], Li, Y.S.[Yun-Song], Gao, X.B.[Xin-Bo],
BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images,
RS(17), No. 3, 2025, pp. 388.
DOI Link 2502
BibRef

Li, X.T.[Xiao-Ting], Duan, W.[Wei], Fu, X.[Xikai], Lv, X.L.[Xiao-Lei],
R-SABMNet: A YOLOv8-Based Model for Oriented SAR Ship Detection with Spatial Adaptive Aggregation,
RS(17), No. 3, 2025, pp. 551.
DOI Link 2502
BibRef

Chen, Y.L.[Yu-Lin], Shen, Y.Y.[Yan-Yun], Duan, C.[Chi], Wang, Z.[Zhipan], Mo, Z.[Zewen], Liang, Y.Y.[Ying-Yu], Zhang, Q.L.[Qing-Ling],
Robust and Efficient SAR Ship Detection: An Integrated Despecking and Detection Framework,
RS(17), No. 4, 2025, pp. 580.
DOI Link 2502
BibRef

Xue, J.[Junfan], Yin, J.J.[Jun-Jun], Yang, J.[Jian],
MtAD-Net: Multi-Threshold Adaptive Decision Net for Unsupervised Synthetic Aperture Radar Ship Instance Segmentation,
RS(17), No. 4, 2025, pp. 593.
DOI Link 2502
BibRef

Zhang, M.J.[Ming-Jin], Zhu, Y.F.[Ying-Feng], Li, L.[Longyi], Guo, J.[Jie], Liu, Z.K.[Zheng-Kun], Li, Y.S.[Yun-Song],
S4Det: Breadth and Accurate Sine Single-Stage Ship Detection for Remote Sense SAR Imagery,
RS(17), No. 5, 2025, pp. 900.
DOI Link 2503
BibRef

Yu, C.[Chushi], Shin, Y.[Yoan],
SMEP-DETR: Transformer-Based Ship Detection for SAR Imagery with Multi-Edge Enhancement and Parallel Dilated Convolutions,
RS(17), No. 6, 2025, pp. 953.
DOI Link 2503
BibRef

Xu, X.[Xiaowo], Zhang, X.L.[Xiao-Ling], Wei, S.[Shunjun], Shi, J.[Jun], Zhang, W.[Wensi], Zhang, T.W.[Tian-Wen], Zhan, X.[Xu], Xu, Y.Q.[Yan-Qin], Zeng, T.J.[Tian-Jiao],
DiffSARShipInst: Diffusion model for ship instance segmentation from synthetic aperture radar imagery,
PandRS(223), 2025, pp. 440-455.
Elsevier DOI 2504
Synthetic aperture radar (SAR), Ship instance segmentation, Diffusion model BibRef

Zhan, S.[Siyu], Zhong, M.[Muge], Yang, Y.X.[Yu-Xuan], Lu, G.M.[Guo-Ming], Zhou, X.Y.[Xin-Yu],
MFT-Reasoning RCNN: A Novel Multi-Stage Feature Transfer Based Reasoning RCNN for Synthetic Aperture Radar (SAR) Ship Detection,
RS(17), No. 7, 2025, pp. 1170.
DOI Link 2504
BibRef

Chen, Z.[Zheng], Zhang, Y.X.[Yu-Xiang], Bai, J.[Jing], Hou, B.[Biao],
EFCNet: Expert Feature-Based Convolutional Neural Network for SAR Ship Detection,
RS(17), No. 7, 2025, pp. 1239.
DOI Link 2504
BibRef

Xu, F.[Fan], Chen, C.[Chuibin], Shang, Z.[Zhigao], Ma, K.K.[Kai-Kuang], Wu, Q.H.[Qi-Hui], Lin, Z.B.[Ze-Bin], Zhan, J.[Jie], Shi, Y.Z.[Yi-Zhou],
Deep Multi-Modal Ship Detection and Classification Network,
CirSysVideo(35), No. 5, May 2025, pp. 4256-4270.
IEEE DOI 2505
Marine vehicles, Radar, Radar imaging, Feature extraction, Artificial intelligence, Radar detection, Detectors, Cameras, automatic identification system BibRef

Zuo, W.K.[Wei-Kang], Fang, S.H.[Sheng-Hui],
TPNet: A High-Performance and Lightweight Detector for Ship Detection in SAR Imagery,
RS(17), No. 9, 2025, pp. 1487.
DOI Link 2505
BibRef

Zhang, M.J.[Ming-Jin], Ouyang, Y.J.[Yuan-Jun], Yang, M.H.[Ming-Hai], Guo, J.[Jie], Li, Y.S.[Yun-Song],
ORPSD: Outer Rectangular Projection-Based Representation for Oriented Ship Detection in SAR Images,
RS(17), No. 9, 2025, pp. 1511.
DOI Link 2505
BibRef

Yu, H.[Hang], Liu, B.Z.[Bing-Zong], Wang, L.[Lei], Li, T.[Teng],
LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion,
RS(17), No. 9, 2025, pp. 1562.
DOI Link 2505
BibRef

Guan, T.Y.[Tian-Yue], Chang, S.[Sheng], Deng, Y.K.[Yun-Kai], Xue, F.L.[Feng-Li], Wang, C.L.[Chun-Le], Jia, X.X.[Xiao-Xue],
Oriented SAR Ship Detection Based on Edge Deformable Convolution and Point Set Representation,
RS(17), No. 9, 2025, pp. 1612.
DOI Link 2505
BibRef

Qian, L.[Lu], Hu, J.Y.[Jun-Yi], Ren, H.H.[Hao-Hao], Lin, J.[Jie], Luo, X.[Xu], Zou, L.[Lin], Zhou, Y.[Yun],
Cross-Level Adaptive Feature Aggregation Network for Arbitrary-Oriented SAR Ship Detection,
RS(17), No. 10, 2025, pp. 1770.
DOI Link 2505
BibRef

Chen, Y.[Yishuang], Chen, J.[Jie], Sun, L.[Long], Wu, B.[Bocai], Xu, H.[Hui],
AJANet: SAR Ship Detection Network Based on Adaptive Channel Attention and Large Separable Kernel Adaptation,
RS(17), No. 10, 2025, pp. 1745.
DOI Link 2505
BibRef

Zhu, H.B.[Hai-Bin], Mu, Y.X.[Ya-Xin], Xie, W.[Wupeng], Xing, K.[Kang], Tan, B.[Bin], Zhou, Y.[Yashi], Yu, Z.D.[Zhong-De], Cui, Z.Y.[Zhi-Ying], Zhang, C.[Chuang], Liu, X.[Xin], Xia, Z.H.[Zheng-Huan],
Deep Metric Learning for Fine-Grained Ship Classification in SAR Images with Sidelobe Interference,
RS(17), No. 11, 2025, pp. 1835.
DOI Link 2506
BibRef

Gu, Y.[Yu], Fang, M.[Minding], Peng, D.L.[Dong-Liang],
TIAR-SAR: An Oriented SAR Ship Detector Combining a Task Interaction Head Architecture with Composite Angle Regression,
RS(17), No. 12, 2025, pp. 2049.
DOI Link 2506
BibRef

He, S.[Shun], Yuan, R.R.[Rui-Rui], Yang, Z.W.[Zhi-Wei], Liu, J.X.[Jia-Xue],
Multiscale Task-Decoupled Oriented SAR Ship Detection Network Based on Size-Aware Balanced Strategy,
RS(17), No. 13, 2025, pp. 2257.
DOI Link 2507
BibRef

Li, F.[Fan], Yu, K.[Kun], Yuan, C.[Chao], Tian, Y.C.[Yi-Chen], Yang, G.[Guang], Yin, K.[Kai], Li, Y.[Youguang],
Dark Ship Detection via Optical and SAR Collaboration: An Improved Multi-Feature Association Method Between Remote Sensing Images and AIS Data,
RS(17), No. 13, 2025, pp. 2201.
DOI Link 2507
BibRef

Zhao, Y.L.[Yu-Liang], Du, Y.[Yang], Wang, Q.[Qiutong], Li, C.H.[Chang-He], Miao, Y.[Yan], Wang, T.F.[Teng-Fei], Song, X.Y.[Xiang-Yu],
LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position-Morphology Matching Mechanism,
RS(17), No. 14, 2025, pp. 2514.
DOI Link 2508
BibRef

Su, C.[Can], Yang, W.[Wei], Pan, Y.C.[Yong-Chen], Zeng, H.C.[Hong-Cheng], Wang, Y.M.[Ya-Min], Chen, J.[Jie], Huang, Z.X.[Zhi-Xiang], Xiong, W.[Wei], Chen, J.[Jie], Li, C.S.[Chun-Sheng],
An Efficient Ship Target Integrated Imaging and Detection Framework (ST-IIDF) for Space-Borne SAR Echo Data,
RS(17), No. 15, 2025, pp. 2545.
DOI Link 2508
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Elsevier DOI 2509
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Elsevier DOI 2511
Fractal domain deep learning, Fractal signal processing, SAR image recognition, Fractal transformer, Learnalble fractal filtering BibRef

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SAR ship detection, Transformer, Complex-valued data, Feature fusion BibRef

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IEEE DOI 2512
Marine vehicles, Artificial intelligence, Discrete wavelet transforms, Remote sensing, Monitoring, regressive analysis BibRef

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Fu, M.Q.[Meng-Qin], Zhang, W.[Wencong], Quan, X.C.[Xiao-Chen], Shi, D.[Dahu], Tan, L.[Luowei], Zhang, J.[Jia], Xing, Y.H.[Ying-Hui], Zhang, S.Z.[Shi-Zhou],
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SAR-NanoShipNet, SAR imagery, Small ship detection, Deep learning BibRef

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IEEE DOI 2211
Training, Location awareness, Image segmentation, Stochastic processes, Radar imaging, Predictive models, Synthetic aperture radar BibRef

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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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SAR Ship Target Detection for SSDv2 under Complex Backgrounds,
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

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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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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Ship Detection in Polarimetric Radar, SAR, PolSAR .