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Ship Detection and Maritime Monitoring Based on SAR Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 20682

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Special Issue Editors

Dr. Marivi Tello

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Guest Editor

Continental-ADC Automotive Distance Control Systems GmbH, Robert-Bosch-Straße 7, D-85521 Riemerling, Germany
Interests: radar based target detection; SAR tomography; compressive sensing; multisensor configurations; cognitive radar; MIMO; automotive radar

Dr. Domenico Velotto

E-Mail Website
Guest Editor

MARUM, Center for Marine Environmental Sciences, University of Bremen, 28359 Bremen, Germany
Interests: synthetic aperture radar; oceanography; signal processing; image processing; pollution and target detection
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Dear Colleagues,

Ship detection and monitoring is essential for a wide range of purposes, such as maritime surveillance, traffic and migration control, and environmental protection.

Since the launch of Seasat in 1978, synthetic aperture radars have proven to be a unique tool for ship detection and maritime monitoring, able to provide high-resolution data and large spatial coverage, and are capable of operating even at night, in the presence of clouds and with no cooperation from the targets. Additionally, the pool of satellites with a SAR sensor onboard has significantly increased in the last two decades, thus dramatically improving the temporal coverage. Since 2014, the Vessel Detection System (VDS) has widely demonstrated the benefits of SAR sensors for ship monitoring, particularly in identifying and pursuing illegal unreported and unregulated fishing activities in areas where they entail economic, social, and environmental threats.

Nevertheless, several challenges remain. For instance, detection performance is sometimes insufficient and strongly dependent on sea state conditions, the size and material of the vessel, data acquisition parameters (incidence angle, polarization, frequency band, etc.), and inherent characteristics of SAR data (speckle, layover, etc.). Additionally, the number of false positives is still too high in some cases, particularly in coastal areas, and the intervention of a trained human operator is still a required critical stage. Furthermore, the absence of extensive ground truth often prevents the practical demonstration, validation, and comparison of methods and algorithms.

New algorithms and processing capabilities, recent advances in novel SAR modes, new uses of constellations of small SAR satellites, and other innovative solutions are promising resources to overcome the remaining challenges in this exciting research field.

Dr. Marivi Tello
Dr. Domenico Velotto
Guest Editors

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Keywords

radar-based target detection
SAR tomography
compressive sensing
multisensor configurations
cognitive radar
MIMO
automotive radar

Published Papers (5 papers)

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Research

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23 pages, 10693 KiB

Open AccessArticle

A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images

by Zequn Sun, Chunning Meng, Jierong Cheng, Zhiqing Zhang and Shengjiang Chang

Remote Sens. 2022, 14(24), 6312; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246312 - 13 Dec 2022

Cited by 14 | Viewed by 2525

Abstract

In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models. Full article

(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)

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10 pages, 5345 KiB

Open AccessCommunication

SynthWakeSAR: A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea

by Igor G. Rizaev and Alin Achim

Remote Sens. 2022, 14(16), 3999; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163999 - 17 Aug 2022

Cited by 9 | Viewed by 4096

Abstract

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46,080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate the classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classifications of vessel types based on their SAR signatures are highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets, respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms. Full article

(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)

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20 pages, 3662 KiB

Open AccessArticle

High-Resolution Wide-Swath Ambiguous Synthetic Aperture Radar Modes for Ship Monitoring

by Nertjana Ustalli and Michelangelo Villano

Remote Sens. 2022, 14(13), 3102; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133102 - 28 Jun 2022

Cited by 7 | Viewed by 1805

Abstract

This paper proposes two high-resolution, wide-swath synthetic aperture radar (SAR) acquisition modes for ship monitoring that tolerate ambiguities and do not require digital beamforming. Both modes, referred to as the low pulse repetition frequency (PRF) and the staggered (high PRF) ambiguous modes, make use of a wide elevation beam, which can be obtained by phase tapering. The first mode is a conventional stripmap mode with a PRF much lower than the nominal Doppler bandwidth, allowing for the imaging of a large swath, because the ships’ azimuth ambiguities can be recognized as they appear at known positions. The second mode exploits a continuous variation of the pulse repetition interval, with a mean PRF greater than the nominal Doppler bandwidth as the range ambiguities of the ships are smeared and are unlikely to determine false alarms. Both modes are thought to operate in open sea surveillance, monitoring Exclusive Economic Zones or international waters. Examples of implementation of both modes for TerraSAR-X show that ground swaths of 120 km or 240 km can be mapped with 2 m² resolution, ensuring outstanding detection performance even for small ships. The importance of resolution over noise and ambiguity level was highlighted by a comparison with ScanSAR modes that image comparable swaths with better noise and ambiguity levels but coarser resolutions. Full article

(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)

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20 pages, 76944 KiB

Open AccessArticle

First Results on Wake Detection in SAR Images by Deep Learning

by Roberto Del Prete, Maria Daniela Graziano and Alfredo Renga

Remote Sens. 2021, 13(22), 4573; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224573 - 14 Nov 2021

Cited by 11 | Viewed by 6923

Abstract

Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer vision tasks, its usage for automatic target recognition (ATR) in SAR images to support MDA is still limited to the detection of ships rather than ship wakes. A dataset is presented in this paper and several state-of-the-art object detectors based on convolutional neural networks (CNNs) are tested with different backbones. The dataset, including more than 250 wake chips, is realized by visually inspecting Sentinel-1 images over highly trafficked maritime sites. Extensive experiments are shown to characterize CNNs for the wake detection task. For the first time, a deep-learning approach is implemented to specifically detect ship wakes without any a-priori knowledge or cuing about the location of the vessel that generated the wake. No annotated dataset was available to train deep-learning detectors on this task, which is instead presented in this paper. Moreover, the benchmarks achieved for different detectors point out promising features and weak points of the relevant approaches. Thus, the work also aims at stimulating more research in this promising, but still under-investigated, field. Full article

(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)

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Review

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36 pages, 8036 KiB

Open AccessReview

A Comprehensive Survey on SAR ATR in Deep-Learning Era

by Jianwei Li, Zhentao Yu, Lu Yu, Pu Cheng, Jie Chen and Cheng Chi

Remote Sens. 2023, 15(5), 1454; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051454 - 05 Mar 2023

Cited by 9 | Viewed by 3629

Abstract

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field. Full article

(This article belongs to the Special Issue Ship Detection and Maritime Monitoring Based on SAR Data)

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