Nighttime fishing vessel observation in Bohai Sea based on VIIRS fishing vessel detection product (VBD)

Li, Yiping; Song, Lu; Zhao, Songfang; Zhao, Dongyang; Wu, Yingchao; You, Guangran; Kong, Zhongren; Xi, Xiaohui; Yu, Zhe

doi:10.1016/j.fishres.2022.106539

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Generally, when examining seasonal trends, spatial distribution patterns, and tracking and monitoring of night-time fishing vessels based on only VBD products, a typical choice is to select the bright targets with a QF of 1 from VBD data [48]. However, exclusively extracting detection results with the QF = 1 from VBD for the above study may inevitably result in a loss of result accuracy.…”

Section: Vbd Versus Enhanced Dna-netmentioning

confidence: 99%

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

Zuo,

Zhou,

Meng

et al. 2024

Remote Sensing

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Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, Pd, Fa, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

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Section: Vbd Versus Enhanced Dna-netmentioning

confidence: 99%

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

Zuo,

Zhou,

Meng

et al. 2024

Remote Sensing

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“…Synthetic aperture radar (SAR) and optical imagery from satellites facilitate a multitude of image-processing techniques for detecting and classifying vessels in the images [32][33][34], including identification of fishing vessels [12] and fishing activity [35]. Lights used by some fisheries to attract target species can also be detected using the visible infrared imaging radiometer suite (VIIRS) and used to map the spatial and temporal extent of activity [35,36] and identify discrete vessel positions [37,38]. However, the temporal lag of vessel detections across subsequent images complicates route prediction and enforcement response on the water.…”

Section: Related Workmentioning

confidence: 99%

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

Cope¹,

Tougher²,

Zetterlind³

et al. 2023

Remote Sensing

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Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas.

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