22.9.4.2 ATR -- Vehicles, Aerial Images, Ship Detection

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

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

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, computer vision, maritime navigation, video, signal, processing 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

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

Graziano, M.D.[Maria Daniela], Grasso, M.[Marco], d'Errico, M.[Marco],
Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
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

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

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

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

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

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

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

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

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

Sun, Y.X., Liu, P., Jin, Y.Q.,
Ship Wake Components: Isolation, Reconstruction, and Characteristics Analysis in Spectral, Spatial, and TerraSAR-X Image Domains,
GeoRS(56), No. 7, July 2018, pp. 4209-4224.
IEEE DOI 1807
Dispersion, Image reconstruction, Kelvin, Marine vehicles, Shape, Spectral analysis, Synthetic aperture radar, synthetic aperture radar (SAR) image BibRef

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Xu, X.D.[Xiao-Dong], Li, W.[Wei], Ran, Q.[Qiong], Du, Q.[Qian], Gao, L.R.[Lian-Ru], Zhang, B.[Bing],
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network,
GeoRS(56), No. 2, February 2018, pp. 937-949.
IEEE DOI 1802
Convolution, Data mining, Feature extraction, Laser radar, Neural networks, Remote sensing, Support vector machines, hyperspectral imagery (HSI) BibRef

Shi, Q.Q.[Qiao-Qiao], Li, W.[Wei], Tao, R.[Ran], Sun, X.[Xu], Gao, L.R.[Lian-Ru],
Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
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, 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

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

Zhang, S.M.[Shao-Ming], Wu, R.[Ruize], Xu, K.[Kunyuan], 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

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

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.[Wenqiang], 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

Tings, B.[Björn], Pleskachevsky, A.[Andrey], Velotto, D.[Domenico], Jacobsen, S.[Sven],
Extension of Ship Wake Detectability Model for Non-Linear Influences of Parameters Using Satellite Based X-Band Synthetic Aperture Radar,
RS(11), No. 5, 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

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

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

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

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

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

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, computer vision 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

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

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

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

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

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

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

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

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
BibRef

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

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

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

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.[Jinghong], 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.[Zhichao], 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
BibRef

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

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

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

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

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

Feng, Y.[Yingchao], 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
BibRef

Cruz, G., Bernardino, A.,
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IEEE DOI 1909
Feature extraction, Aircraft, Boats, Visualization, Video sequences, Detectors, Monitoring, Object detection, recurrent neural networks, remote monitoring BibRef

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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],
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Cao, C.H.[Cheng-Hui], Zhang, J.[Jie], Meng, J.M.[Jun-Min], Zhang, X.[Xi], Mao, X.P.[Xing-Peng],
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Ma, J.L.[Jin-Lei], Zhou, Z.Q.[Zhi-Qiang], Wang, B.[Bo], Zong, H.[Hua], Wu, F.[Fei],
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Ribeiro, R., Cruz, G., Matos, J., Bernardino, A.,
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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

Yang, J.Y.[Jun-Ying], Qiu, X.L.[Xiao-Lan], Zhong, L.H.[Li-Hua], Shang, M.Y.[Ming-Yang], Ding, C.B.[Chi-Biao],
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Zhang, T.W.[Tian-Wen], Zhang, X.L.[Xiao-Ling], Shi, J.[Jun], Wei, S.J.[Shun-Jun],
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Xiao, X.[Xiaowu], 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,
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Cui, Z., Li, Q., Cao, Z., Liu, N.,
Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images,
GeoRS(57), No. 11, November 2019, pp. 8983-8997.
IEEE DOI 1911
Marine vehicles, Feature extraction, Radar polarimetry, Synthetic aperture radar, Radar imaging, Semantics, synthetic aperture radar (SAR) BibRef

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

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

Gao, F.[Fei], Shi, W.[Wei], Wang, J.[Jun], Yang, E.[Erfu], Zhou, H.Y.[Hui-Yu],
Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images,
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Fan, W.W.[Wei-Wei], Zhou, F.[Feng], Bai, X.[Xueru], Tao, M.L.[Ming-Liang], Tian, T.[Tian],
Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images,
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Ai, J., Tian, R., Luo, Q., Jin, J., Tang, B.,
Multi-Scale Rotation-Invariant Haar-Like Feature Integrated CNN-Based Ship Detection Algorithm of Multiple-Target Environment in SAR Imagery,
GeoRS(57), No. 12, December 2019, pp. 10070-10087.
IEEE DOI 1912
Marine vehicles, Feature extraction, Clutter, Synthetic aperture radar, Radar polarimetry, Correlation, constant false alarm rate (CFAR) detector (TCS-JCFAR)-based prescreening BibRef

Malambo, L.[Lonesome], Popescu, S.[Sorin], Ku, N.W.[Nian-Wei], Rooney, W.[William], Zhou, T.[Tan], Moore, S.[Samuel],
A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting,
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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,
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Liu, G.[Genwang], Zhang, X.[Xi], Meng, J.[Junmin],
A Small Ship Target Detection Method Based on Polarimetric SAR,
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Du, J., Hu, X., Sun, Y.,
Adaptive Robust Nonlinear Control Design for Course Tracking of Ships Subject to External Disturbances and Input Saturation,
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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],
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Liang, Y.[Yi], Sun, K.[Kun], Zeng, Y.[Yugui], Li, G.[Guofei], Xing, M.D.[Meng-Dao],
An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images,
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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],
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Wei, S.J.[Shun-Jun], Su, H.[Hao], Ming, J.[Jing], Wang, C.[Chen], Yan, M.[Min], Kumar, D.[Durga], Shi, J.[Jun], Zhang, X.L.[Xiao-Ling],
Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet,
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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],
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Liu, T., Zhang, J., Gao, G., Yang, J., Marino, A.,
CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter,
GeoRS(58), No. 1, January 2020, pp. 58-81.
IEEE DOI 2001
Marine vehicles, Speckle, Covariance matrices, Clutter, Synthetic aperture radar, Probability density function, synthetic aperture radar BibRef

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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Zhang, W.[Wen], He, X.[Xujie], Li, W.[Wanyi], Zhang, Z.[Zhi], Luo, Y.[Yongkang], Su, L.[Li], Wang, P.[Peng],
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Ship segmentation, Sea fog, Classification, Interference Factor Discriminator, Ship extractor BibRef

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A Method for Active Marine Target Detection Based on Complex Interferometric Dissimilarity in Dual-Channel ATI-SAR Systems,
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IEEE DOI 2001
Object detection, Synthetic aperture radar, Measurement, Marine vehicles, Clutter, Radar polarimetry, synthetic aperture radar (SAR) BibRef

Zhang, L.[Ling], Mao, D.W.[Dong-Wei], Niu, J.[Jiong], Wu, Q.M.J.[Q. M. Jonathan], Ji, Y.G.[Yong-Gang],
Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM,
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High frequency surface wave radar. marine surveillance. BibRef

Wang, Z.[Ziwei], Yang, T.[Ting], Zhang, H.[Hong],
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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],
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Karakus, O., Rizaev, I., Achim, A.,
Ship Wake Detection in SAR Images via Sparse Regularization,
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IEEE DOI 2003
Generalized minimax concave (GMC) regularization, inverse problem, maximum a posteriori (MAP) estimation, synthetic aperture radar (SAR) imagery BibRef

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,
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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,
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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,
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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),
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IEEE DOI 2004
Airborne, interferometric, microwave sensor, passive, ship detection BibRef

Guo, R.[Rui], Cui, J.Y.[Jing-Yu], Jing, G.B.[Guo-Bin], Zhang, S.X.[Shuang-Xi], Xing, M.D.[Meng-Dao],
Validating GEV Model for Reflection Symmetry-Based Ocean Ship Detection with Gaofen-3 Dual-Polarimetric Data,
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Sun, W.F.[Wei-Feng], Ji, M.J.[Meng-Jie], Huang, W.M.[Wei-Min], Ji, Y.G.[Yong-Gang], Dai, Y.[Yongshou],
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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,
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IEEE DOI 2005
Maritime traffic service networks, intelligent maritime transportation, knowledge based systems, sensor systems BibRef

Xiao, Y.[Yijia], Chen, Y.M.[Yan-Ming], Liu, X.Q.[Xiao-Qiang], Yan, Z.J.[Zhao-Jin], Cheng, L.[Liang], Li, M.[Manchun],
Oil Flow Analysis in the Maritime Silk Road Region Using AIS Data,
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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,
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Li, Y.Z.[Yong-Zhen], Quan, S.[Sinong], Xiang, D.L.[De-Liang], Wang, W.[Wei], Hu, C.B.[Can-Bin], Liu, Y.[Yemin], Wang, X.[Xuesong],
Ship Recognition from Chaff Clouds with Sophisticated Polarimetric Decomposition,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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Chen, S.Q.[Shi-Qi], Zhang, J.[Jun], Zhan, R.H.[Rong-Hui],
R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images,
RS(12), No. 12, 2020, pp. xx-yy.
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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,
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Graziano, M.D.[Maria Daniela], Renga, A.[Alfredo], Moccia, A.[Antonio],
Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness,
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DOI Link 1910
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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