Ship detection and classification from optical remote sensing images: A survey

Li, Bo; Xie, Xiaoyang; Wei, Xingxing; Tang, Wenting

doi:10.1016/j.cja.2020.09.022

Cited by 113 publications

(33 citation statements)

References 97 publications

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“…When remote sensing satellites are used to monitor fishing vessels, optical remote sensing and Synthetic Aperture Rader (SAR) images are used. Because of their high spatial resolution, optical satellites can directly monitor and identify fishing vessels during the daytime and obtain considerable information about the appearance of the vessels [3][4][5]. At night, information on the distribution of fishing vessels is obtained by detecting their lights (such as fish-collecting lights) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Guan

Zhang

et al. 2021

Remote Sensing

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In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS.

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Section: Introductionmentioning

confidence: 99%

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Guan

Zhang

et al. 2021

Remote Sensing

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show abstract

“…For VHR images, a large amount of literature exists, with the number of works following the increasing number of sensors and the quantity of publicly available data [7,8]. Many of these approaches focused on detecting ships with classical image processing pipelines: image processing using spectral indices or histograms (e.g., sea-land segmentation, cloud removal), ship candidate extraction (e.g., threshold, anomaly detection, saliency), and, then, rule-based ship identification or classification using statistical methods.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning

Ciocarlan

Stoian

2021

Remote Sensing

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Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multi-spectral images with few labeled examples. We design a network architecture for detecting ships with a backbone that can be pre-trained separately. By using self supervised learning, an emerging unsupervised training procedure, we learn good features on Sentinel-2 images, without requiring labeling, to initialize our network’s backbone. The full network is then fine-tuned to learn to detect ships in challenging settings. We evaluated this approach versus pre-training on ImageNet and versus a classical image processing pipeline. We examined the impact of variations in the self-supervised learning step and we show that in the few-shot learning setting self-supervised pre-training achieves better results than ImageNet pre-training. When enough training data are available, our self-supervised approach is as good as ImageNet pre-training. We conclude that a better design of the self-supervised task and bigger non-annotated dataset sizes can lead to surpassing ImageNet pre-training performance without any annotation costs.

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“…e researches of ANN methods in maritime surveillance made the horizon line detection easy, accurate, and robust. For optical remote sensing images applied in maritime, Li et al [20] summarized the detection and classification of ship optical remote sensing images. Both methods were analyzed for traditional feature-designed methods and the deep convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

Survey on Deep Learning-Based Marine Object Detection

Zhang

et al. 2021

Journal of Advanced Transportation

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We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.

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Ship detection and classification from optical remote sensing images: A survey

Cited by 113 publications

References 97 publications

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning

Survey on Deep Learning-Based Marine Object Detection

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