Remote Sensing for Maritime Traffic Understanding

The capability of prompt response in the case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective, a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding, or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed Information and Communication Technology framework devoted to understanding the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of (i) detecting the presence of vessel targets in the input imagery, (ii) estimating the vessel types on the basis of their geometric and scatterometric features, (iii) estimating the vessel kinematics, (iv) classifying the navigation behavior of the vessel and predicting its route, and, eventually, (v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public annotated datasets.

[1]  Tianwen Zhang,et al.  Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images , 2023, Journal of Applied Remote Sensing.

[2]  Peng Liu,et al.  A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images , 2023, Remote. Sens..

[3]  Cheng Chi,et al.  A Comprehensive Survey on SAR ATR in Deep-Learning Era , 2023, Remote. Sens..

[4]  D. R. Panuju,et al.  Application of Landsat-8 OLI/TIRS to assess the Urban Heat Island (UHI) , 2022, IOP Conference Series: Earth and Environmental Science.

[5]  Zhe Zeng,et al.  Ship detection based on deep learning using SAR imagery: a systematic literature review , 2022, Soft Computing.

[6]  Andrea Marchetti,et al.  Towards the Evaluation of Date Time Features in a Ship Route Prediction Model , 2022, Journal of Marine Science and Engineering.

[7]  P. Heiselberg,et al.  Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning , 2022, Sensors.

[8]  E. Tijan,et al.  Using Automatic Identification System Data in Vessel Route Prediction and Seaport Operations , 2021, Journal of Maritime & Transportation Science.

[9]  Jinwan Park,et al.  Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data , 2021, Journal of Marine Science and Engineering.

[10]  A. Mancini,et al.  A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities † , 2021, Sensors.

[11]  Alexander Bespalov,et al.  Predicting Ship Trajectory Based on Neural Networks Using AIS Data , 2021, Journal of Marine Science and Engineering.

[12]  Raffaella Guida,et al.  Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance , 2020, Remote. Sens..

[13]  Andrzej Stateczny,et al.  Remote Sensing in Vessel Detection and Navigation , 2020, Sensors.

[14]  Lokukaluge P. Perera,et al.  A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data , 2020, Ocean Engineering.

[15]  Andrea Marchetti,et al.  Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta , 2020, J. Syst. Inf. Technol..

[16]  Haipeng Wang,et al.  FUSAR-Ship: building a high-resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition , 2020, Science China Information Sciences.

[17]  Guillaume Hajduch,et al.  Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning , 2019, Remote. Sens..

[18]  Antonio Moccia,et al.  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 , 2019, Remote. Sens..

[19]  Yangfan Huang,et al.  Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis , 2019, Applied Sciences.

[20]  Jia Duan,et al.  Ship Classification Methods for Sentinel-1 SAR Images , 2019, CSPS.

[21]  Massimo Martinelli,et al.  Remote Sensing for Maritime Prompt Monitoring , 2019, Journal of Marine Science and Engineering.

[22]  C. Guedes Soares,et al.  Ship trajectory uncertainty prediction based on a Gaussian Process model , 2019, Ocean Engineering.

[23]  Moncef Gabbouj,et al.  Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks , 2019, Remote. Sens..

[24]  Lauren Biermann,et al.  Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea , 2019, Remote. Sens..

[25]  Luigi Bedini,et al.  Multi-Sensor Satellite Data Processing for Marine Traffic Understanding , 2019, Electronics.

[26]  Weihai Li,et al.  Ship Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset , 2018, Sensors.

[27]  Hirofumi Aoki,et al.  CNN-based ship classification method incorporating SAR geometry information , 2018, Remote Sensing.

[28]  Hong Zhang,et al.  Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets , 2018, Sensors.

[29]  Mihai Datcu,et al.  Moving Ship Velocity Estimation Using TanDEM-X Data Based on Subaperture Decomposition , 2018, IEEE Geoscience and Remote Sensing Letters.

[30]  Jie Li,et al.  Study on the Combined Application of CFAR and Deep Learning in Ship Detection , 2018, Journal of the Indian Society of Remote Sensing.

[31]  Luigi Bedini,et al.  Synthetic Aperture Radar Processing for Vessel Kinematics Estimation , 2018, IWCIM@EUSIPCO.

[32]  Haitao Lang,et al.  Ship Classification in Moderate-Resolution SAR Image by Naive Geometric Features-Combined Multiple Kernel Learning , 2017, IEEE Geoscience and Remote Sensing Letters.

[33]  Harm Greidanus,et al.  The exploitation of Sentinel-1 images for vessel size estimation , 2016 .

[34]  Lily Rachmawati,et al.  Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology , 2016, IEEE Transactions on Intelligent Transportation Systems.

[35]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[36]  Antonio Moccia,et al.  Use of Doppler Parameters for Ship Velocity Computation in SAR Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Xi Zhang,et al.  Ship Classification in SAR Image by Joint Feature and Classifier Selection , 2016, IEEE Geoscience and Remote Sensing Letters.

[38]  Fabio Del Frate,et al.  Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Peijun Du,et al.  Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features , 2015 .

[40]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[41]  Kazuo Ouchi,et al.  Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics , 2013, Remote. Sens..

[42]  Maarten van Someren,et al.  Machine learning for vessel trajectories using compression, alignments and domain knowledge , 2012, Expert Syst. Appl..

[43]  Lokukaluge P. Perera,et al.  Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[44]  Palanisamy Shanmugam,et al.  Ship Recognition by Integration of SAR and AIS , 2012 .

[45]  Marco Martorella,et al.  Classification of Man-Made Targets via Invariant Coherency-Matrix Eigenvector Decomposition of Polarimetric SAR/ISAR Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Gerard Margarit,et al.  Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[47]  K. Gade A Non-singular Horizontal Position Representation , 2010 .

[48]  Jordi J. Mallorquí,et al.  Single-Pass Polarimetric SAR Interferometry for Vessel Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[49]  R. Keith Raney,et al.  On the use of permanent symmetric scatterers for ship characterization , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[50]  R. K. Hawkins,et al.  Ship detection and characterization using polarimetric SAR , 2004 .

[51]  E. Salerno Using Low-Resolution SAR Scattering Features for Ship Classification , 2022, IEEE Geoscience and Remote Sensing Letters.

[52]  Xiaoling Zhang,et al.  Injection of Traditional Hand-Crafted Features into Modern CNN-Based Models for SAR Ship Classification: What, Why, Where, and How , 2021, Remote. Sens..

[53]  Lanqing Huang,et al.  OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[54]  Domenico Velotto,et al.  Ship Classification in TerraSAR-X Images With Convolutional Neural Networks , 2018, IEEE Journal of Oceanic Engineering.

[55]  Branko Ristic,et al.  Detecting Anomalies from a Multitarget Tracking Output , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[56]  L. Breiman Random Forests , 2001, Machine Learning.