23.8.4.3 ATR -- Vehicles, Aerial Images, Ship Detection

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

Boat Detection,
Online2019
HTML Version. Dataset, Ships. 1911

WWW Link. Public video dataset for boat detection/tracking from UAV video footage
See also MULTIDRONE.
See also Racing Bicycle Detection/Tracking from UAV Footage, UAV Detection. BibRef

Lee, H.J.[Hsi-Jian], Huang, L.F.[Lung-Fa], Chen, Z.[Zen],
Multi-frame ship detection and tracking in an infrared image sequence,
PR(23), No. 7, 1990, pp. 785-798.
Elsevier DOI 0401
BibRef

Zhu, C., Zhou, H., Wang, R., Guo, J.,
A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features,
GeoRS(48), No. 9, September 2010, pp. 3446-3456.
IEEE DOI 1008
BibRef

Liu, Z.Y.[Zhao-Ying], Zhou, F.[Fugen], Chen, X.W.[Xiao-Wu], Bai, X.Z.[Xiang-Zhi], Sun, C.M.[Chang-Ming],
Iterative infrared ship target segmentation based on multiple features,
PR(47), No. 9, 2014, pp. 2839-2852.
Elsevier DOI 1406
Infrared ship target BibRef

Liu, Z.Y.[Zhao-Ying], Bai, X.Z.[Xiang-Zhi], Sun, C.M.[Chang-Ming], Zhou, F.[Fugen], Li, Y.J.[Yu-Jian],
Infrared ship target segmentation through integration of multiple feature maps,
IVC(48-49), No. 1, 2016, pp. 14-25.
Elsevier DOI 1604
Infrared images BibRef

Bai, X.Z.[Xiang-Zhi], Chen, Z.G.[Zhi-Guo], Zhang, Y.[Yu], Liu, Z.Y.[Zhao-Ying], Lu, Y.[Yi],
Infrared Ship Target Segmentation Based on Spatial Information Improved FCM,
Cyber(46), No. 12, December 2016, pp. 3259-3271.
IEEE DOI 1612
BibRef
Earlier:
Spatial information based FCM for infrared ship target segmentation,
ICIP14(5127-5131)
IEEE DOI 1502
Euclidean distance Active contours BibRef

Shi, Z., Yu, X., Jiang, Z., Li, B.,
Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature,
GeoRS(52), No. 8, August 2014, pp. 4511-4523.
IEEE DOI 1403
Detectors BibRef

Teng, F.[Fei], Liu, Q.[Qing],
Multi-scale ship tracking via random projections,
SIViP(8), No. 6, September 2014, pp. 1069-1076.
WWW Link. 1408
BibRef

Teng, F.[Fei], Liu, Q.[Qing],
Robust multi-scale ship tracking via multiple compressed features fusion,
SP:IC(31), No. 1, 2015, pp. 76-85.
Elsevier DOI 1502
Compressive sensing theory BibRef

Tang, J.X.[Jie-Xiong], Deng, C.W.[Chen-Wei], Huang, G.B.[Guang-Bin], Zhao, B.J.[Bao-Jun],
Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine,
GeoRS(53), No. 3, March 2015, pp. 1174-1185.
IEEE DOI 1412
compressed sensing BibRef

Elvidge, C.D.[Christopher D.], Zhizhin, M.[Mikhail], Baugh, K.[Kimberly], Hsu, F.C.[Feng-Chi],
Automatic Boat Identification System for VIIRS Low Light Imaging Data,
RS(7), No. 3, 2015, pp. 3020-3036.
DOI Link 1504
BibRef

Wu, F.[Fan], Wang, C.[Chao], Jiang, S.F.[Shao-Feng], Zhang, H.[Hong], Zhang, B.[Bo],
Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features,
RS(7), No. 5, 2015, pp. 5511-5533.
DOI Link 1506
BibRef

Holtzhausen, P.J., Crnojevic, V., Herbst, B.M.,
An illumination invariant framework for real-time foreground detection,
RealTimeIP(10), No. 2, June 2015, pp. 423-433.
WWW Link. 1506
BibRef
Earlier:
The detection of naval vessels by fusion of edge and color background models,
IPTA12(147-152)
IEEE DOI 1503
Gaussian processes BibRef

Gómez-Enri, J.[Jesús], Scozzari, A.[Andrea], Soldovieri, F.[Francesco], Coca, J.[Josep], Vignudelli, S.[Stefano],
Detection and Characterization of Ship Targets Using CryoSat-2 Altimeter Waveforms,
RS(8), No. 3, 2016, pp. 193.
DOI Link 1604
BibRef

Gómez-Enri, J.[Jesús], Cipollini, P., Passaro, M., Vignudelli, S.[Stefano], Tejedor, B., Coca, J.[Josep],
Coastal Altimetry Products in the Strait of Gibraltar,
GeoRS(54), No. 9, September 2016, pp. 5455-5466.
IEEE DOI 1609
height measurement BibRef

Zou, Z., Shi, Z.,
Ship Detection in Spaceborne Optical Image With SVD Networks,
GeoRS(54), No. 10, October 2016, pp. 5832-5845.
IEEE DOI 1610
neural nets BibRef

Heiselberg, H.[Henning],
A Direct and Fast Methodology for Ship Recognition in Sentinel-2 Multispectral Imagery,
RS(8), No. 12, 2016, pp. 1033.
DOI Link 1612
BibRef

Dijk, J.[Judith], Schutte, K.[Klamer], Nieuwenhuizen, R.[Robert], Gagnon, M.A.[Marc-André], Gagnon, J.P.[Jean-Philippe], Tremblay, P.[Pierre], Savary, S.[Simon], Farley, V.[Vincent], Lagueux, P.[Philippe], Chamberland, M.[Martin],
Infrared hyperspectral imaging of ship plumes,
SPIE(Newsroom), November 22, 2016.
DOI Link 1612
Characterizing the spectral features of exhaust gases via IR hyperspectral detection enables the standoff detection of distant ships. BibRef

Gan, S., Liang, S., Li, K., Deng, J., Cheng, T.,
Long-Term Ship Speed Prediction for Intelligent Traffic Signaling,
ITS(18), No. 1, January 2017, pp. 82-91.
IEEE DOI 1701
Communication system signaling BibRef

Gan, S., Liang, S., Li, K., Deng, J., Cheng, T.,
Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach,
ITS(19), No. 2, February 2018, pp. 426-435.
IEEE DOI 1802
Intelligent transportation systems, Marine vehicles, Prediction algorithms, Predictive models, Rivers, intelligent traffic signalling system (ITSS) BibRef

Patroumpas, K.[Kostas], Alevizos, E.[Elias], Artikis, A.[Alexander], Vodas, M.[Marios], Pelekis, N.[Nikos], Theodoridis, Y.[Yannis],
Online event recognition from moving vessel trajectories,
GeoInfo(21), No. 2, April 2017, pp. 389-427.
Springer DOI 1702
BibRef

Bloisi, D.D., Previtali, F., Pennisi, A., Nardi, D., Fiorini, M.,
Enhancing Automatic Maritime Surveillance Systems With Visual Information,
ITS(18), No. 4, April 2017, pp. 824-833.
IEEE DOI 1704
Artificial intelligence BibRef

Xu, F.[Fang], Liu, J.H.[Jing-Hong], Sun, M.C.[Ming-Chao], Zeng, D.D.[Dong-Dong], Wang, X.[Xuan],
A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Xu, F.[Fang], Liu, J.H.[Jing-Hong], Dong, C.[Chao], Wang, X.[Xuan],
Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Dong, C.[Chao], Liu, J.H.[Jing-Hong], Xu, F.[Fang],
Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Dong, L., Wang, B., Zhao, M., Xu, W.,
Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering,
GeoRS(55), No. 5, May 2017, pp. 3037-3050.
IEEE DOI 1705
fog, geophysical image processing, image filtering, object detection, ocean waves, remote sensing, antivibration pipeline-filtering algorithm, image background smoothness, image border, infrared imager, infrared maritime target detection method, multiframe-based clutter removal method, ocean wave, pipeline-filtering model, saliency map, saliency singularity evaluation, sea fog, sea glint, BibRef

He, H., Lin, Y., Chen, F., Tai, H.M., Yin, Z.,
Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting,
GeoRS(55), No. 6, June 2017, pp. 3091-3107.
IEEE DOI 1706
Marine vehicles, Remote sensing, Robustness, Satellites, Shape, Surveillance, Inshore ship detection, pose weighted voting, radial gradient angle (RGA), satellite image, shape-similar, distractor BibRef

Lin, H.N.[Hao-Ning], Shi, Z.W.[Zhen-Wei], Zou, Z.X.[Zheng-Xia],
Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
sea-land segmentation and ship detection. BibRef

Nie, T.[Ting], He, B.[Bin], Bi, G.[Guoling], Zhang, Y.[Yu], Wang, W.[Wensheng],
A Method of Ship Detection under Complex Background,
IJGI(6), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Prasad, D.K., Rajan, D., Rachmawati, L., Rajabally, E., Quek, C.,
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey,
ITS(18), No. 8, August 2017, pp. 1993-2016.
IEEE DOI 1708
Cameras, Image edge detection, Intelligent sensors, Marine vehicles, Object detection, Radar tracking, Maritime vehicles, autonomous automobiles, maritime navigation, video, signal, processing BibRef

Yao, S.[Shun], Chang, X.L.[Xue-Li], Cheng, Y.F.[Yu-Feng], Jin, S.Y.[Shu-Ying], Zuo, D.S.[De-Shan],
Detection of Moving Ships in Sequences of Remote Sensing Images,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Yang, X.[Xue], Sun, H.[Hao], Fu, K.[Kun], Yang, J.R.[Ji-Rui], Sun, X.[Xian], Yan, M.L.[Meng-Long], Guo, Z.[Zhi],
Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Fu, K.[Kun], Chang, Z.H.[Zhong-Han], Zhang, Y.[Yue], Xu, G.L.[Guang-Luan], Zhang, K.[Keshu], Sun, X.[Xian],
Rotation-Aware and Multi-Scale Convolutional Neural Network for Object Detection in Remote Sensing Images,
PandRS(161), 2020, pp. 294-308.
Elsevier DOI 2002
Convolutional neural networks, Objection detection, Remote sensing images, Rotation aware, Multi-scale BibRef

Fu, K.[Kun], Chen, Z.[Zhuo], Zhang, Y.[Yue], Sun, X.[Xian],
Enhanced Feature Representation in Detection for Optical Remote Sensing Images,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Gallego, A.J.[Antonio-Javier], Pertusa, A.[Antonio], Gil, P.[Pablo],
Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Shao, Z., Wu, W., Wang, Z., Du, W., Li, C.,
SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection,
MultMed(20), No. 10, October 2018, pp. 2593-2604.
IEEE DOI 1810
image segmentation, object detection, ships, video signal processing, video surveillance, ship types, SeaShips, ship detection BibRef

Li, Q., Mou, L., Liu, Q., Wang, Y., Zhu, X.X.,
HSF-Net: Multiscale Deep Feature Embedding for Ship Detection in Optical Remote Sensing Imagery,
GeoRS(56), No. 12, December 2018, pp. 7147-7161.
IEEE DOI 1812
Marine vehicles, Feature extraction, Remote sensing, Object detection, Proposals, Optical sensors, Optical imaging, remote sensing BibRef

Zhu, C.Y.[Chen-Yang], Garcia, H.[Heriberto], Kaplan, A.[Anna], Schinault, M.[Matthew], Handegard, N.O.[Nils Olav], Godø, O.R.[Olav Rune], Huang, W.[Wei], Ratilal, P.[Purnima],
Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Zhao, M., Yao, X., Sun, J., Zhang, S., Bai, J.,
GIS-Based Simulation Methodology for Evaluating Ship Encounters Probability to Improve Maritime Traffic Safety,
ITS(20), No. 1, January 2019, pp. 323-337.
IEEE DOI 1901
Marine vehicles, Transportation, Analytical models, Accidents, Object oriented modeling, Geographic information systems, Safety, maritime traffic safety BibRef

Alessandrini, A., Mazzarella, F., Vespe, M.,
Estimated Time of Arrival Using Historical Vessel Tracking Data,
ITS(20), No. 1, January 2019, pp. 7-15.
IEEE DOI 1901
Artificial intelligence, Marine vehicles, Estimation, Safety, Security, Data mining, Radar tracking, Estimated time of arrival, port operations BibRef

Fu, K.[Kun], Li, Y.[Yang], Sun, H.[Hao], Yang, X.[Xue], Xu, G.L.[Guang-Luan], Li, Y.T.[Yu-Ting], Sun, X.[Xian],
A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Wang, N.[Nan], Li, B.[Bo], Xu, Q.Z.[Qi-Zhi], Wang, Y.H.[Yong-Hua],
Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Wang, Y.Y.[Yuan-Yuan], Wang, C.[Chao], Zhang, H.[Hong], Dong, Y.[Yingbo], Wei, S.[Sisi],
Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Zhang, S.M.[Shao-Ming], Wu, R.Z.[Rui-Ze], Xu, K.Y.[Kun-Yuan], Wang, J.M.[Jian-Mei], Sun, W.W.[Wei-Wei],
R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Wen, Y.Q.[Yuan-Qiao], Zhang, Y.M.[Yi-Meng], Huang, L.[Liang], Zhou, C.H.[Chun-Hui], Xiao, C.S.[Chang-Shi], Zhang, F.[Fan], Peng, X.[Xin], Zhan, W.Q.[Wen-Qiang], Sui, Z.Y.[Zhong-Yi],
Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Yao, Y.[Yuan], Jiang, Z.G.[Zhi-Guo], Zhang, H.[Haopeng], Zhou, Y.[Yu],
On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Le Caillec, J.M.[Jean-Marc], Habonneau, J.[Jérôme], Khenchaf, A.[Ali],
Ship Profile Imaging Using Multipath Backscattering,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Prasad, D.K., Prasath, C.K., Rajan, D., Rachmawati, L., Rajabally, E., Quek, C.,
Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods,
ITS(20), No. 5, May 2019, pp. 1787-1802.
IEEE DOI 1905
Adaptation models, Videos, Gaussian distribution, Object detection, Benchmark testing, Cameras, Vehicle dynamics, Maritime vehicles BibRef

Wang, L., Zhang, M., Chen, J.,
Investigation on the Electromagnetic Scattering From the Accurate 3-D Breaking Ship Waves Generated by CFD Simulation,
GeoRS(57), No. 5, May 2019, pp. 2689-2699.
IEEE DOI 1905
computational fluid dynamics, electromagnetic wave scattering, flow simulation, geometry, iterative methods, physical optics, ships, composite scattering BibRef

Hsu, F.C.[Feng-Chi], Elvidge, C.D.[Christopher D.], Baugh, K.[Kimberly], Zhizhin, M.[Mikhail], Ghosh, T.[Tilottama], Kroodsma, D.[David], Susanto, A.[Adi], Budy, W.[Wiryawan], Riyanto, M.[Mochammad], Nurzeha, R.[Ridwan], Sudarja, Y.[Yeppi],
Cross-Matching VIIRS Boat Detections with Vessel Monitoring System Tracks in Indonesia,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Wang, W.X.[Wen-Xiu], Fu, Y.T.[Yu-Tian], Dong, F.[Feng], Li, F.[Feng],
Semantic segmentation of remote sensing ship image via a convolutional neural networks model,
IET-IPR(13), No. 6, 10 May 2019, pp. 1016-1022.
DOI Link 1906
BibRef

Zhou, H., Jiang, T.,
Decision Tree Based Sea-Surface Weak Target Detection With False Alarm Rate Controllable,
SPLetters(26), No. 6, June 2019, pp. 793-797.
IEEE DOI 1906
decision trees, feature extraction, fractals, learning (artificial intelligence), object detection, decision tree BibRef

Liu, Z.Q.[Zhi-Quan],
Practical backstepping control for underactuated ship path following associated with disturbances,
IET-ITS(13), No. 5, May 2019, pp. 834-840.
DOI Link 1906
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Wen, G., Ge, S.S., Chen, C.L.P., Tu, F., Wang, S.,
Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique,
Cyber(49), No. 9, Sep. 2019, pp. 3420-3431.
IEEE DOI 1907
adaptive control, control nonlinearities, control system synthesis, feedback, surface vessel BibRef

Dong, C.[Chao], Liu, J.H.[Jing-Hong], Xu, F.[Fang], Liu, C.L.[Cheng-Long],
Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Wang, B., Motai, Y., Dong, L., Xu, W.,
Detecting Infrared Maritime Targets Overwhelmed in Sun Glitters by Antijitter Spatiotemporal Saliency,
GeoRS(57), No. 7, July 2019, pp. 5159-5173.
IEEE DOI 1907
Sun, Spatiotemporal phenomena, Visualization, Jitter, Object detection, Imaging, Generators, Image segmentation, target detection BibRef

Yan, Y.M.[Yi-Ming], Tan, Z.C.[Zhi-Chao], Su, N.[Nan],
A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
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Feng, Y.C.[Ying-Chao], Diao, W.H.[Wen-Hui], Sun, X.[Xian], Yan, M.L.[Meng-Long], Gao, X.[Xin],
Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
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Cruz, G., Bernardino, A.,
Learning Temporal Features for Detection on Maritime Airborne Video Sequences Using Convolutional LSTM,
GeoRS(57), No. 9, September 2019, pp. 6565-6576.
IEEE DOI 1909
Feature extraction, Aircraft, Boats, Visualization, Video sequences, Detectors, Monitoring, Object detection, recurrent neural networks, remote monitoring BibRef

Zhou, X.Y.[Xing-Yue], Yang, K.[Kunde], Duan, R.[Rui],
Deep Learning Based on Striation Images for Underwater and Surface Target Classification,
SPLetters(26), No. 9, September 2019, pp. 1378-1382.
IEEE DOI 1909
belief networks, convolutional neural nets, image classification, interference (signal), learning (artificial intelligence), sonar images BibRef

You, Y.[Yanan], Li, Z.[Zezhong], Ran, B.[Bohao], Cao, J.Y.[Jing-Yi], Lv, S.[Sudi], Liu, F.[Fang],
Broad Area Target Search System for Ship Detection via Deep Convolutional Neural Network,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
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Ma, J.L.[Jin-Lei], Zhou, Z.Q.[Zhi-Qiang], Wang, B.[Bo], Zong, H.[Hua], Wu, F.[Fei],
Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909
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Ribeiro, R., Cruz, G., Matos, J., Bernardino, A.,
A Data Set for Airborne Maritime Surveillance Environments,
CirSysVideo(29), No. 9, September 2019, pp. 2720-2732.
IEEE DOI 1909
Cameras, Surveillance, Aircraft, Boats, Hyperspectral imaging, Labeling, Image databases, hyperspectral imaging, video surveillance BibRef

Xiao, X.W.[Xiao-Wu], Zhou, Z.Q.[Zhi-Qiang], Wang, B.[Bo], Li, L.H.[Lin-Hao], Miao, L.J.[Ling-Juan],
Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
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Chen, H., Gao, T., Chen, W., Zhang, Y., Zhao, J.,
Contour Refinement and EG-GHT-Based Inshore Ship Detection in Optical Remote Sensing Image,
GeoRS(57), No. 11, November 2019, pp. 8458-8478.
IEEE DOI 1911
Marine vehicles, Feature extraction, Head, Shape, Indexes, Training, Strain, Border scoring, curvature filtering, structured binarization feature (SBF) BibRef

Du, J., Hu, X., Sun, Y.,
Adaptive Robust Nonlinear Control Design for Course Tracking of Ships Subject to External Disturbances and Input Saturation,
SMCS(50), No. 1, January 2020, pp. 193-202.
IEEE DOI 2001
Marine vehicles, Adaptive systems, Control design, Nonlinear dynamical systems, Adaptation models, Navigation, ship steering system BibRef

Tian, T.[Tian], Pan, Z.H.[Zhi-Hong], Tan, X.Y.[Xiang-Yu], Chu, Z.Q.[Zheng-Quan],
Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
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Wu, Y.[Yue], Ma, W.P.[Wen-Ping], Gong, M.[Maoguo], Bai, Z.F.[Zhuang-Fei], Zhao, W.[Wei], Guo, Q.Q.[Qiong-Qiong], Chen, X.B.[Xiao-Bo], Miao, Q.G.[Qi-Guang],
A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
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Nie, T.[Ting], Han, X.[Xiyu], He, B.[Bin], Li, X.S.[Xian-Sheng], Liu, H.X.[Hong-Xing], Bi, G.L.[Guo-Ling],
Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
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Ruiz, J.[Javier], Caballero, I.[Isabel], Navarro, G.[Gabriel],
Sensing the Same Fishing Fleet with AIS and VIIRS: A Seven-Year Assessment of Squid Jiggers in FAO Major Fishing Area 41,
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DOI Link 2001
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Zhang, W.[Wen], He, X.J.[Xu-Jie], Li, W.Y.[Wan-Yi], Zhang, Z.[Zhi], Luo, Y.K.[Yong-Kang], Su, L.[Li], Wang, P.[Peng],
An integrated ship segmentation method based on discriminator and extractor,
IVC(93), 2020, pp. 103824.
Elsevier DOI 2001
Ship segmentation, Sea fog, Classification, Interference Factor Discriminator, Ship extractor BibRef

Wang, Z.W.[Zi-Wei], Yang, T.[Ting], Zhang, H.[Hong],
Land contained sea area ship detection using spaceborne image,
PRL(130), 2020, pp. 125-131.
Elsevier DOI 2002
Synthetic aperture radar, Image understanding, Ship detection, Land contained sea area BibRef

Chen, Y.T.[Yan-Tong], Li, Y.Y.[Yu-Yang], Wang, J.S.[Jun-Sheng], Chen, W.N.[Wei-Nan], Zhang, X.Z.[Xian-Zhong],
Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Zhang, T.[Tao], Zhao, S.[Shuai], Cheng, B.[Bo], Chen, J.L.[Jun-Liang],
Detection of AIS Closing Behavior and MMSI Spoofing Behavior of Ships Based on Spatiotemporal Data,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
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Shao, Z., Wang, L., Wang, Z., Du, W., Wu, W.,
Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video,
CirSysVideo(30), No. 3, March 2020, pp. 781-794.
IEEE DOI 2003
Marine vehicles, Feature extraction, Real-time systems, Surveillance, Visualization, Object detection, Remote sensing, CNN BibRef

Xie, X.Y.[Xiao-Yang], Li, B.[Bo], Wei, X.X.[Xing-Xing],
Ship Detection in Multispectral Satellite Images Under Complex Environment,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
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Xiao, Z., Fu, X., Zhang, L., Goh, R.S.M.,
Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey,
ITS(21), No. 5, May 2020, pp. 1796-1825.
IEEE DOI 2005
Maritime traffic service networks, intelligent maritime transportation, knowledge based systems, sensor systems BibRef

Xiao, Y.J.[Yi-Jia], Chen, Y.M.[Yan-Ming], Liu, X.Q.[Xiao-Qiang], Yan, Z.J.[Zhao-Jin], Cheng, L.[Liang], Li, M.C.[Man-Chun],
Oil Flow Analysis in the Maritime Silk Road Region Using AIS Data,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
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Song, J.[Juyoung], Kim, D.J.[Duk-Jin], Kang, K.M.[Ki-Mook],
Automated Procurement of Training Data for Machine Learning Algorithm on Ship Detection Using AIS Information,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
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Chénier, R.[René], Sagram, M.[Mesha], Omari, K.[Khalid], Jirovec, A.[Adam],
Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
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Guo, H., Yang, X., Wang, N., Song, B., Gao, X.,
A Rotational Libra R-CNN Method for Ship Detection,
GeoRS(58), No. 8, August 2020, pp. 5772-5781.
IEEE DOI 2007
Marine vehicles, Feature extraction, Proposals, Object detection, Reliability, Semantics, Machine learning, Deep learning, ship detection BibRef

Zhou, A., Xie, W., Pei, J.,
Background Modeling in the Fourier Domain for Maritime Infrared Target Detection,
CirSysVideo(30), No. 8, August 2020, pp. 2634-2649.
IEEE DOI 2008
Biological system modeling, Adaptation models, Heuristic algorithms, Gaussian distribution, Entropy, Correlation, entropy filter BibRef

Farahnakian, F.[Fahimeh], Heikkonen, J.[Jukka],
Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008
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Park, J.J.[Jae-Jin], Kim, T.S.[Tae-Sung], Park, K.A.[Kyung-Ae], Oh, S.[Sangwoo], Lee, M.[Moonjin], Foucher, P.Y.[Pierre-Yves],
Application of Spectral Mixture Analysis to Vessel Monitoring Using Airborne Hyperspectral Data,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
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Chen, L.Q.[Li-Qiong], Shi, W.X.[Wen-Xuan], Fan, C.[Cien], Zou, L.[Lian], Deng, D.X.[De-Xiang],
A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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Zhang, Y.L.[Yu-Lian], Guo, L.H.[Li-Hong], Wang, Z.F.[Zeng-Fa], Yu, Y.[Yang], Liu, X.W.[Xin-Wei], Xu, F.[Fang],
Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
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Hu, J.M.[Jian-Ming], Zhi, X.Y.[Xi-Yang], Zhang, W.[Wei], Ren, L.F.[Long-Fei], Bruzzone, L.[Lorenzo],
Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
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Tang, G.[Gang], Liu, S.[Shibo], Fujino, I.[Iwao], Claramunt, C.[Christophe], Wang, Y.[Yide], Men, S.[Shaoyang],
H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
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Villa, J.[Jose], Aaltonen, J.[Jussi], Virta, S.[Sauli], Koskinen, K.T.[Kari T.],
A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
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Hass, F.S.[Frederik Seerup], Arsanjani, J.J.[Jamal Jokar],
Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg-Ship Discrimination,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012
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Yu, H., Fang, Z., Murray, A.T., Peng, G.,
A Direction-Constrained Space-Time Prism-Based Approach for Quantifying Possible Multi-Ship Collision Risks,
ITS(22), No. 1, January 2021, pp. 131-141.
IEEE DOI 2012
Marine vehicles, Accidents, Predictive models, Collision avoidance, Spatiotemporal phenomena, Geography, Navigation, ship path optimization BibRef

Cui, Z., Wang, X., Liu, N., Cao, Z., Yang, J.,
Ship Detection in Large-Scale SAR Images Via Spatial Shuffle-Group Enhance Attention,
GeoRS(59), No. 1, January 2021, pp. 379-391.
IEEE DOI 2012
Marine vehicles, Radar polarimetry, Object detection, Feature extraction, Synthetic aperture radar, Semantics, synthetic aperture radar (SAR) BibRef

Li, L., Zhou, Z., Wang, B., Miao, L., Zong, H.,
A Novel CNN-Based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box,
GeoRS(59), No. 1, January 2021, pp. 686-699.
IEEE DOI 2012
Marine vehicles, Feature extraction, Remote sensing, Proposals, Object detection, Optical imaging, Optical sensors, ship detection BibRef

Shan, Y., Zhou, X., Liu, S., Zhang, Y., Huang, K.,
SiamFPN: A Deep Learning Method for Accurate and Real-Time Maritime Ship Tracking,
CirSysVideo(31), No. 1, January 2021, pp. 315-325.
IEEE DOI 2101
Target tracking, Radar tracking, Marine vehicles, Proposals, Correlation, Visualization, Cameras, Visual tracking, region proposal network BibRef

Liu, Q., Xiang, X., Yang, Z., Hu, Y., Hong, Y.,
Arbitrary Direction Ship Detection in Remote-Sensing Images Based on Multitask Learning and Multiregion Feature Fusion,
GeoRS(59), No. 2, February 2021, pp. 1553-1564.
IEEE DOI 2101
Marine vehicles, Remote sensing, Proposals, Feature extraction, Shape, Head, Object detection, Convolutional neural network (CNN), ship detection BibRef

Wang, Z.Q.[Zhen-Qing], Zhou, Y.[Yi], Wang, F.[Futao], Wang, S.X.[Shi-Xin], Xu, Z.Y.[Zhi-Yu],
SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Di, Y.H.[Yang-Hua], Jiang, Z.G.[Zhi-Guo], Zhang, H.[Haopeng],
A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
Dataset, Ships. BibRef

Chen, L.Q.[Li-Qiong], Shi, W.X.[Wen-Xuan], Deng, D.X.[De-Xiang],
Improved YOLOv3 Based on Attention Mechanism for Fast and Accurate Ship Detection in Optical Remote Sensing Images,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Iancu, B.[Bogdan], Soloviev, V.[Valentin], Zelioli, L.[Luca], Lilius, J.[Johan],
ABOships: An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Liu, Z.Y.[Zhao-Ying], Waqas, M.[Muhammad], Yang, J.[Jia], Rashid, A.[Ahmar], Han, Z.[Zhu],
A Multi-Task CNN for Maritime Target Detection,
SPLetters(28), 2021, pp. 434-438.
IEEE DOI 2103
Dataset, Ship Detection. MaRine ShiP (MRSP-13) Dataset. Marine vehicles, Task analysis, Object detection, Image segmentation, Boats, Feature extraction, Annotations, cross-layer connections BibRef

Zhang, X., Wang, G., Zhu, P., Zhang, T., Li, C., Jiao, L.,
GRS-Det: An Anchor-Free Rotation Ship Detector Based on Gaussian-Mask in Remote Sensing Images,
GeoRS(59), No. 4, April 2021, pp. 3518-3531.
IEEE DOI 2104
Marine vehicles, Detectors, Feature extraction, Proposals, Task analysis, Object detection, Remote sensing, ship detection BibRef

Geng, X.M.[Xiao-Meng], Shi, L.[Lei], Yang, J.[Jie], Li, P.X.[Ping-Xiang], Zhao, L.[Lingli], Sun, W.D.[Wei-Dong], Zhao, J.Q.[Jin-Qi],
Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Tian, L.[Ling], Cao, Y.[Yu], He, B.[Bokun], Zhang, Y.F.[Yi-Fan], He, C.[Chu], Li, D.[Deshi],
Image Enhancement Driven by Object Characteristics and Dense Feature Reuse Network for Ship Target Detection in Remote Sensing Imagery,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Lee, M.J.[Myung-Jun], Kim, J.E.[Ji-Eun], Ryu, B.H.[Bo-Hyun], Kim, K.T.[Kyung-Tae],
Robust Maritime Target Detector in Short Dwell Time,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Gao, G.S.[Guang-Shuai], Liu, Q.J.[Qing-Jie], Wang, Y.H.[Yun-Hong],
Counting From Sky: A Large-Scale Data Set for Remote Sensing Object Counting and a Benchmark Method,
GeoRS(59), No. 5, May 2021, pp. 3642-3655.
IEEE DOI 2104
Remote sensing, Task analysis, Buildings, Marine vehicles, Convolution, Neural networks, Object detection, scale pyramid module (SPM) BibRef

Wang, N.[Nan], Li, B.[Bo], Wei, X.X.[Xing-Xing], Wang, Y.H.[Yong-Hua], Yan, H.Q.[Huan-Qian],
Ship Detection in Spaceborne Infrared Image Based on Lightweight CNN and Multisource Feature Cascade Decision,
GeoRS(59), No. 5, May 2021, pp. 4324-4339.
IEEE DOI 2104
Marine vehicles, Feature extraction, Remote sensing, Satellites, Object detection, Neural networks, Gaussian distribution, multivariate Gaussian distribution BibRef

Jiang, J.H.[Jia-Huan], Fu, X.J.[Xiong-Jun], Qin, R.[Rui], Wang, X.Y.[Xiao-Yan], Ma, Z.F.[Zhi-Feng],
High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Yang, Y.[Yi], Pan, Z.X.[Zong-Xu], Hu, Y.X.[Yu-Xin], Ding, C.[Chibiao],
CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Tan, Z.B.[Zhen-Biao], Zhang, Z.K.[Ze-Kun], Xing, T.Z.[Ting-Zhuang], Huang, X.[Xiao], Gong, J.B.[Jun-Bin], Ma, J.[Jie],
Exploit Direction Information for Remote Ship Detection,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Abreu, F.H.O.[Fernando H. O.], Soares, A.[Amilcar], Paulovich, F.V.[Fernando V.], Matwin, S.[Stan],
A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
BibRef

You, Y.[Yanan], Ran, B.H.[Bo-Hao], Meng, G.[Gang], Li, Z.Z.[Ze-Zhong], Liu, F.[Fang], Li, Z.X.[Zhi-Xin],
OPD-Net: Prow Detection Based on Feature Enhancement and Improved Regression Model in Optical Remote Sensing Imagery,
GeoRS(59), No. 7, July 2021, pp. 6121-6137.
IEEE DOI 2106
Marine vehicles, Feature extraction, Remote sensing, Detectors, Proposals, Optical imaging, Optical sensors, Deep learning, remote sensing BibRef

Li, Y.Y.[Yang-Yang], Mao, H.[Heting], Liu, R.J.[Rui-Jiao], Pei, X.[Xuan], Jiao, L.C.[Li-Cheng], Shang, R.H.[Rong-Hua],
A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
Ships, etc. BibRef

Zheng, Y.B.[Yong-Bin], Sun, P.[Peng], Zhou, Z.T.[Zong-Tan], Xu, W.Y.[Wan-Ying], Ren, Q.[Qiang],
ADT-Det: Adaptive Dynamic Refined Single-Stage Transformer Detector for Arbitrary-Oriented Object Detection in Satellite Optical Imagery,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Liu, B.[Bo], Xiao, Q.[Qi], Zhang, Y.[Yuhao], Ni, W.[Wei], Yang, Z.[Zhen], Li, L.G.[Li-Gang],
Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Hu, J.M.[Jian-Ming], Zhi, X.Y.[Xi-Yang], Shi, T.J.[Tian-Jun], Zhang, W.[Wei], Cui, Y.[Yang], Zhao, S.G.[Sheng-Gang],
PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, L.H.[Lin-Hao], Zhou, Z.Q.[Zhi-Qiang], Wang, B.[Bo], Miao, L.J.[Ling-Juan], An, Z.[Zhe], Xiao, X.W.[Xiao-Wu],
Domain Adaptive Ship Detection in Optical Remote Sensing Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Dong, Y.X.[Yu-Xin], Chen, F.K.[Fu-Kun], Han, S.[Shuang], Liu, H.[Hao],
Ship Object Detection of Remote Sensing Image Based on Visual Attention,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Cui, Z.Y.[Zhen-Yu], Leng, J.X.[Jia-Xu], Liu, Y.[Ying], Zhang, T.L.[Tian-Lin], Quan, P.[Pei], Zhao, W.[Wei],
SKNet: Detecting Rotated Ships as Keypoints in Optical Remote Sensing Images,
GeoRS(59), No. 10, October 2021, pp. 8826-8840.
IEEE DOI 2109
Marine vehicles, Detectors, Feature extraction, Remote sensing, Optical imaging, Optical detectors, Object detection, Keypoints, ship detection BibRef

Wu, J.[Jin], Cao, C.Q.[Chang-Qing], Zhou, Y.D.[Yue-Dong], Zeng, X.D.[Xiao-Dong], Feng, Z.J.[Zhe-Jun], Wu, Q.F.[Qi-Fan], Huang, Z.Q.[Zi-Qiang],
Multiple Ship Tracking in Remote Sensing Images Using Deep Learning,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liang, M.[Maohan], Zhan, Y.[Yang], Liu, R.W.[Ryan Wen],
MVFFNet: Multi-view feature fusion network for imbalanced ship classification,
PRL(151), 2021, pp. 26-32.
Elsevier DOI 2110
Ship classification, Multi-view feature fusion, Imbalanced data, CAE, BiGRU BibRef

Wu, J.X.[Ji-Xiang], Pan, Z.X.[Zong-Xu], Lei, B.[Bin], Hu, Y.X.[Yu-Xin],
LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Qu, Z.F.[Zhen-Fang], Zhu, F.Z.[Fu-Zhen], Qi, C.X.[Cheng-Xiao],
Remote Sensing Image Target Detection: Improvement of the YOLOv3 Model with Auxiliary Networks,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Huang, C.[Chuan], Li, Z.Y.[Zhong-Yu], Lou, M.Y.[Ming-Yue], Qiu, X.Y.[Xing-Ye], An, H.Y.[Hong-Yang], Wu, J.J.[Jun-Jie], Yang, J.Y.[Jian-Yu], Huang, W.[Wei],
BeiDou-Based Passive Radar Vessel Target Detection: Method and Experiment via Long-Time Optimized Integration,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
Power limits in navigation signal. BibRef

Yang, Z.Q.[Zhi-Qing], Zhou, H.[Hao], Tian, Y.[Yingwei], Huang, W.M.[Wei-Min], Shen, W.[Wei],
Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ciocarlan, A.[Alina], Stoian, A.[Andrei],
Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Hu, J.M.[Jian-Ming], Zhi, X.[Xiyang], Shi, T.J.[Tian-Jun], Yu, L.J.[Li-Jian], Zhang, W.[Wei],
Ship Detection via Dilated Rate Search and Attention-Guided Feature Representation,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Liu, J.M.[Jin-Ming], Chen, H.[Hao], Wang, Y.[Yu],
Multi-Source Remote Sensing Image Fusion for Ship Target Detection and Recognition,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Liu, T.[Tao], Jiang, Y.[Yanni], Marino, A.[Armando], Gao, G.[Gui], Yang, J.[Jian],
The Polarimetric Detection Optimization Filter and its Statistical Test for Ship Detection,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI 2112
Marine vehicles, Detectors, Clutter, Synthetic aperture radar, Covariance matrices, Speckle, Sea state, constant false alarm rate (CFAR) BibRef

Yu, Y.[Ying], Yang, X.[Xi], Li, J.[Jie], Gao, X.B.[Xin-Bo],
A Cascade Rotated Anchor-Aided Detector for Ship Detection in Remote Sensing Images,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Marine vehicles, Feature extraction, Detectors, Remote sensing, Object detection, Customer relationship management, Training, ship detection BibRef

He, B.[Boyong], Li, X.[Xianjiang], Huang, B.[Bo], Gu, E.[Enhui], Guo, W.J.[Wei-Jie], Wu, L.[Liaoni],
UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
Dataset, Ship Detection. BibRef

Li, W.X.[Wei-Xin], Li, M.[Ming], Zuo, L.[Lei], Sun, H.[Hao], Chen, H.[Hongmeng], Li, Y.[Yachao],
Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Feng, J.J.[Jun-Jian], Li, B.[Bin], Tian, L.F.[Lian-Fang], Dong, C.[Chao],
Rapid Ship Detection Method on Movable Platform Based on Discriminative Multi-Size Gradient Features and Multi-Branch Support Vector Machine,
ITS(23), No. 2, February 2022, pp. 1357-1367.
IEEE DOI 2202
Marine vehicles, Feature extraction, Support vector machines, Object detection, Visualization, Videos, Movable platform, rapid ship detection BibRef

Zheng, J.C.[Jia-Chun], Sun, S.D.[Shi-Dan], Zhao, S.J.[Shi-Jia],
Fast ship detection based on lightweight YOLOv5 network,
IET-IPR(16), No. 6, 2022, pp. 1585-1593.
DOI Link 2204
BibRef

Guo, H.W.[Hong-Wei], Bai, H.Y.[Hong-Yang], Yuan, Y.[Yuman], Qin, W.W.[Wei-Wei],
Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Li, L.Y.[Li-Yuan], Jiang, L.Y.[Lin-Yi], Zhang, J.W.[Jing-Wen], Wang, S.Q.[Si-Qi], Chen, F.S.[Fan-Sheng],
A Complete YOLO-Based Ship Detection Method for Thermal Infrared Remote Sensing Images under Complex Backgrounds,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Li, Y.[Ye], Ren, H.X.[Hong-Xiang],
Visual Analysis of Vessel Behaviour Based on Trajectory Data: A Case Study of the Yangtze River Estuary,
IJGI(11), No. 4, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Xu, C.J.[Chu-Jie], Zheng, X.T.[Xiang-Tao], Lu, X.Q.[Xiao-Qiang],
Multi-Level Alignment Network for Cross-Domain Ship Detection,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zou, H.X.[Huan-Xin], He, S.T.[Shi-Tian], Cao, X.[Xu], Sun, L.[Li], Wei, J.[Juan], Liu, S.[Shuo], Liu, J.[Jian],
Rescaling-Assisted Super-Resolution for Medium-Low Resolution Remote Sensing Ship Detection,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, H.P.[Hao-Peng], Zhang, X.Y.[Xing-Yu], Meng, G.[Gang], Guo, C.[Chen], Jiang, Z.G.[Zhi-Guo],
Few-Shot Multi-Class Ship Detection in Remote Sensing Images Using Attention Feature Map and Multi-Relation Detector,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Huang, L.[Liang], Wang, F.X.[Feng-Xiang], Zhang, Y.[Yalun], Xu, Q.X.[Qing-Xia],
Fine-Grained Ship Classification by Combining CNN and Swin Transformer,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tian, Y.[Yang], Liu, J.H.[Jing-Hong], Zhu, S.J.[Sheng-Jie], Xu, F.[Fang], Bai, G.B.[Guan-Bing], Liu, C.L.[Cheng-Long],
Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Xie, X.Z.[Xiao-Zhu], Li, L.H.[Lin-Hao], An, Z.[Zhe], Lu, G.[Gang], Zhou, Z.Q.[Zhi-Qiang],
Small Ship Detection Based on Hybrid Anchor Structure and Feature Super-Resolution,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, W.S.[Wen-Sheng], Zhang, X.B.[Xin-Bo], Sun, W.[Wu], Huang, M.[Min],
A Novel Method of Ship Detection under Cloud Interference for Optical Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, S.J.[Sheng-Jia], Zhu, H.C.[Hong-Chun], Li, J.[Jie], Yang, Y.[Yanrui], Liu, H.Y.[Hai-Ying],
Data-Free Area Detection and Evaluation for Marine Satellite Data Products,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

He, Y.[Yaomin], Yang, H.[Huizhang], He, H.F.[Hua-Feng], Yin, J.J.[Jun-Jun], Yang, J.[Jian],
A Ship Discrimination Method Based on High-Frequency Electromagnetic Theory,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Kizilkaya, S.[Serdar], Alganci, U.[Ugur], Sertel, E.[Elif],
VHRShips: An Extensive Benchmark Dataset for Scalable Deep Learning-Based Ship Detection Applications,
IJGI(11), No. 8, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Xi, C.P.[Cai-Ping], Liu, R.Q.[Ren-Qiao],
Detection of Small Floating Target on Sea Surface Based on Gramian Angular Field and Improved EfficientNet,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Jian, L.[Ling], Pu, Z.Q.[Zhi-Qi], Zhu, L.[Lili], Yao, T.C.[Tian-Can], Liang, X.[Xijun],
SS R-CNN: Self-Supervised Learning Improving Mask R-CNN for Ship Detection in Remote Sensing Images,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Sun, Y.X.[Yu-Xin], Su, L.[Li], Luo, Y.K.[Yong-Kang], Meng, H.[Hao], Zhang, Z.[Zhi], Zhang, W.[Wen], Yuan, S.Z.[Shou-Zheng],
IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes,
CirSysVideo(32), No. 9, September 2022, pp. 6029-6043.
IEEE DOI 2209
Marine vehicles, Image segmentation, Meteorology, Feature extraction, Interference, Object detection, Visualization, dynamic contour learning BibRef

Chen, Y.T.[Yan-Tong], Zhang, Z.L.[Zhong-Ling], Chen, Z.[Zekun], Zhang, Y.Y.[Yan-Yan], Wang, J.S.[Jun-Sheng],
Fine-Grained Classification of Optical Remote Sensing Ship Images Based on Deep Convolution Neural Network,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Li, J.F.[Jian-Feng], Li, Z.F.[Zong-Feng], Chen, M.X.[Ming-Xu], Wang, Y.L.[Yong-Ling], Luo, Q.H.[Qing-Hua],
A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R3Det,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Tang, G.[Gang], Zhao, H.[Hongren], Claramunt, C.[Christophe], Men, S.[Shaoyang],
FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Zhang, J.[Jun], Huang, R.[Ruofei], Li, Y.[Yan], Pan, B.[Bin],
Oriented Ship Detection Based on Intersecting Circle and Deformable RoI in Remote Sensing Images,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Bakdi, A.[Azzeddine], Vanem, E.[Erik],
Fullest COLREGs Evaluation Using Fuzzy Logic for Collaborative Decision-Making Analysis of Autonomous Ships in Complex Situations,
ITS(23), No. 10, October 2022, pp. 18433-18445.
IEEE DOI 2210
Marine vehicles, Fuzzy logic, Navigation, Regulation, Grounding, Safety, Artificial intelligence, AIS, algorithmic regulation, water-depth to draught ratio BibRef

Guo, Y.[Yue], Chen, S.Q.[Shi-Qi], Zhan, R.[Ronghui], Wang, W.[Wei], Zhang, J.[Jun],
LMSD-YOLO: A Lightweight YOLO Algorithm for Multi-Scale SAR Ship Detection,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Ma, D.D.[Dong-Dong], Dong, L.[Lili], Xu, W.[Wenhai],
Detecting Maritime Infrared Targets in Harsh Environment by Improved Visual Attention Model Preselector and Anti-Jitter Spatiotemporal Filter Discriminator,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Chen, W.M.[Wei-Ming], Han, B.[Bing], Yang, Z.[Zheng], Gao, X.B.[Xin-Bo],
MSSDet: Multi-Scale Ship-Detection Framework in Optical Remote-Sensing Images and New Benchmark,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Li, L.Y.[Li-Yuan], Yu, J.N.[Jia-Ning], Chen, F.S.[Fan-Sheng],
TISD: A Three Bands Thermal Infrared Dataset for All Day Ship Detection in Spaceborne Imagery,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zheng, X.[Xin], Kang, D.[Di], Si, P.[Pengbo], Wu, Q.[Qiang],
Infrared and visible image fusion for ship targets based on scale-aware feature decomposition,
IET-IPR(16), No. 14, 2022, pp. 3977-3987.
DOI Link 2212
BibRef

Zhang, X.C.[Xiao-Cai], Fu, X.J.[Xiu-Ju], Xiao, Z.[Zhe], Xu, H.Y.[Hai-Yan], Qin, Z.[Zheng],
Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond,
ITS(23), No. 11, November 2022, pp. 19980-19998.
IEEE DOI 2212
Trajectory, Transportation, Safety, Predictive models, Hidden Markov models, Support vector machines, Soft sensors, deep learning BibRef

Kong, Z.[Zhan], Cui, Y.[Yaqi], Xiong, W.[Wei], Xiong, Z.Y.[Zhen-Yu], Xu, P.L.[Ping-Liang],
Ship Target Recognition Based on Context-Enhanced Trajectory,
IJGI(11), No. 12, 2022, pp. xx-yy.
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Hua, Z.[Zizheng], Pan, G.F.[Gao-Feng], Gao, K.[Kun], Li, H.[Hengchao], Chen, S.[Su],
AF-OSD: An Anchor-Free Oriented Ship Detector Based on Multi-Scale Dense-Point Rotation Gaussian Heatmap,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Rucinski, M.[Marek], Wozniak, E.[Edyta], Kulczyk, S.[Sylwia], Derek, M.[Marta],
Small Recreational Boat Detection Using Sentinel-1 Data for the Monitoring of Recreational Ecosystem Services,
RS(15), No. 7, 2023, pp. 1807.
DOI Link 2304
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Meng, F.[Fan], Zhao, G.[Guocan], Zhang, G.J.[Guo-Jun], Li, Z.[Zhi], Ding, K.[Kaimeng],
Visual Detection and Association Tracking of Dim Small Ship Targets from Optical Image Sequences of Geostationary Satellite Using Multispectral Radiation Characteristics,
RS(15), No. 8, 2023, pp. 2069.
DOI Link 2305
Wide area. BibRef

Liu, D.[Di], Zhang, Y.[Yan], Zhao, Y.[Yan], Shi, Z.G.[Zhi-Guang], Zhang, J.H.[Jing-Hua], Zhang, Y.[Yu], Ling, F.[Feng], Zhang, Y.[Yi],
AARN: Anchor-guided attention refinement network for inshore ship detection,
IET-IPR(17), No. 7, 2023, pp. 2225-2237.
DOI Link 2305
computer vision, convolutional neural nets, feature extraction, object detection, ships BibRef

Fanizza, V.[Vincenzo], Rijlaarsdam, D.[David], González, P.T.T.[Pablo Tomás Toledano], Espinosa-Aranda, J.L.[José Luis],
Transfer Learning for On-Orbit Ship Segmentation,
AI4Space22(21-36).
Springer DOI 2304
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Zhao, H.[Hangyue], Zhang, H.[Hongpu], Zhao, Y.[Yanyun],
YOLOv7-sea: Object Detection of Maritime UAV Images based on Improved pYOLOv7,
Maritime23(233-238)
IEEE DOI 2302
Sea surface, Visualization, Head, Conferences, Detectors, Object detection, Interference BibRef

Ye, B.[Biaohua], Qin, T.[Tong], Zhou, H.J.[Hua-Jun], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
Cross-level Attention and Ratio Consistency Network for Ship Detection,
ICPR22(4644-4650)
IEEE DOI 2212
Measurement uncertainty, Object detection, Performance gain, Marine vehicles, Task analysis BibRef

Kaur, P.[Parneet], Aziz, A.[Arslan], Jain, D.[Darshan], Patel, H.[Harshil], Hirokawa, J.[Jonathan], Townsend, L.[Lachlan], Reimers, C.[Christoph], Hua, F.[Fiona],
Sea Situational Awareness (SeaSAw) Dataset,
Precognition22(2578-2586)
IEEE DOI 2210
Industries, Technological innovation, Image resolution, Lighting, Object detection, Safety BibRef

Paiano, L.[Luigi], Calabrese, F.[Francesca], Cataldo, M.[Marco], Sebastiani, L.[Luca], Leonardi, N.[Nicola],
Ship Detection and Tracking Based on a Custom Aerial Dataset,
CIAP22(III:425-436).
Springer DOI 2205
BibRef

Luo, Z.Y.[Zi-Yuan], Nguyen, M.[Minh], Yan, W.Q.[Wei Qi],
Sailboat Detection Based on Automated Search Attention Mechanism and Deep Learning Models,
IVCNZ21(1-6)
IEEE DOI 2201
Deep learning, Visualization, Machine learning algorithms, Computational modeling, Object detection, visual object detection BibRef

Marques, T.P.[Tunai Porto], Albu, A.B.[Alexandra Branzan], O'Hara, P.[Patrick], Serra, N.[Norma], Morrow, B.[Ben], McWhinnie, L.[Lauren], Canessa, R.[Rosaline],
Size-invariant Detection of Marine Vessels From Visual Time Series,
WACV21(443-453)
IEEE DOI 2106
Visualization, Time series analysis, Layout, Boats, Detectors, Feature extraction BibRef

Wang, C., Ren, H., Li, H.,
Vessel trajectory prediction based on AIS data and bidirectional GRU,
CVIDL20(260-264)
IEEE DOI 2102
learning (artificial intelligence), marine vehicles, recurrent neural nets, LSTM, Tianjin port, biGRU, Bi-GRU. BibRef

Xie, B.R.[Bao-Rong], Heng, Y.[Ye], Feng, S.Y.[Shu-Yi], Wang, Y.[Yang], Zhu, X.Z.[Xin-Zhong],
Sea-land Segmentation of Infrared Remote Sensing Image Based on Complex Background,
CVIDL20(165-168)
IEEE DOI 2102
geophysical image processing, image matching, image segmentation, infrared imaging, object detection, remote sensing, ships, infrared ship detection BibRef

Nalamati, M., Sharma, N., Saqib, M., Blumenstein, M.,
Automated Monitoring in Maritime Video Surveillance System,
IVCNZ20(1-6)
IEEE DOI 2012
Computational modeling, Object detection, Benchmark testing, Video surveillance, Task analysis, Videos, Meteorology, Intruder detection BibRef

o Wang, Y., Cheng, H., Zhou, X., Luo, W., Zhang, H.,
Moving Ship Detection and Movement Prediction In Remote Sensing Videos,
ISPRS20(B2:1303-1308).
DOI Link 2012
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Santos, C.E., Bhanu, B.,
Dyfusion: Dynamic IR/RGB Fusion for Maritime Vessel Recognition,
ICIP18(1328-1332)
IEEE DOI 1809
Training, Probabilistic logic, Context modeling, Sensor fusion, Pipelines, Robustness, Image recognition, probabilistic models BibRef

Aziz, K.[Kheireddine], Bouchara, F.[Frédéric],
Multimodal Deep Learning for Robust Recognizing Maritime Imagery in the Visible and Infrared Spectrums,
ICIAR18(235-244).
Springer DOI 1807
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Brauchle, J.[Jörg], Bayer, S.[Steven], Berger, R.[Ralf],
Automatic Ship Detection on Multispectral and Thermal Infrared Aerial Images Using MACS-Mar Remote Sensing Platform,
PSIVTWS17(382-395).
Springer DOI 1806
BibRef

Liu, Z., Hu, J., Weng, L., Yang, Y.,
Rotated region based CNN for ship detection,
ICIP17(900-904)
IEEE DOI 1803
Convolution, Feature extraction, Marine vehicles, Object detection, Proposals, Task analysis, Training, Rotated region, ship detection BibRef