Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

Zhong, Laifu; Liu, Xiaosheng; Yang, Peng; Lin, Rizhi

doi:10.1515/geo-2020-0180

Cited by 16 publications

(6 citation statements)

References 55 publications

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“…Obviously, our method achieves more than 95% in both precision and recall in different regions; it proves that the method we proposed can handle complex data situations well and accurately identify most vessel targets. We chose the methods proposed by Elvidge et al [14] and Zhong Liang et al [18] to conduct experiments on our dataset and compare the detection performance with our method. Elvidge's method is designed for low-resolution NTL data, which has a wide influence and has produced a large number of ship target detection products based on DNB data.…”

Section: Resultsmentioning

confidence: 99%

“…Lebona et al [17] applied Constant False Alarm Rate (CFAR), an adaptive threshold method commonly used in Synthetic Aperture Radar (SAR) images, to nighttime remote sensing data. Zhong Liang et al [18] proposed a vessel detection method based on a two-parameter CFAR [19] and conducted it on Luojia1-01 data. Guo Gang et al [20] improved the feature enhancement of radiation differences between light fishing vessels and background pixels using SMI.…”

Section: Introductionmentioning

confidence: 99%

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Vessel Detection with SDGSAT-1 Nighttime Light Images

Zhao,

Qiu,

Chen

et al. 2023

Remote Sensing

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The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vessel Detection with SDGSAT-1 Nighttime Light Images

Zhao,

Qiu,

Chen

et al. 2023

Remote Sensing

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“…For example, to compensate for the loss of astronomical observations that were negatively affected by urban artificial light pollution, ground-based observations of night sky brightness are important for calibrating sky light models [42]. In fields related to ecology, nighttime light data had been used to map human impacts on marine areas, impervious surfaces around the globe, the erosion of speciesrich protected areas by artificial light and even coastlines that are more or less affected by artificial light [43], [44].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spatiotemporal Characteristics and Influencing Factors of Land Urbanization Level in China at Different Scales Based on Nighttime Light Remote Sensing

Zhang,

Liu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Urbanization is an important symbol of regional economic development and social progress. Studying the urbanization level is greatly important for the scientific development of towns and cities, and for the efficient decisionmaking of governments. Nighttime light data, containing information on human activities, economic and social development, compensate for the uncertainty and lag problems of the indicator method. Those data can also provide a more intuitive reflection of the level of urbanization and offer novel insights for research on urbanization. In this study, based on the 2000-2020 EANTLI_Like nighttime light remote sensing data, the characteristics of the spatial and temporal dynamic changes in the land urbanization level in China were measured. Results were analysed at four scales: national, regional, provincial, and municipal. Finally, the main economic indicators affecting urbanization were selected as variables to simply analyze the driving force of land urbanization through spatial autocorrelation and spatio-temporal geographically weighted regression model. Results indicated that China's land urbanization level continued to grow from 2000 to 2020, with an average annual growth rate of 2.51%. The spatial distribution of land urbanization varied significantly across regions.The level of land Urbanization in the eastern coastal region was significantly higher than that in the inland and western provinces, at the agricultural and provincial scales. Hong Kong, Macao, and Shanghai ranked high in terms of land urbanization, and the Pearl River Delta and Yangtze River Delta regions in which they were located also belong to the cluster of high land urbanization levels. Meanwhile, the goodness of fit amongst GDP, the proportion of output of secondary and tertiary industries, freight volume and land urbanization level were higher than 0.86. Among them, GDP and the output of secondary and tertiary industries had a more obvious role in promoting land urbanization. The freight volume exhibited positive effects on land urbanization level in the western region, Heilongjiang and Jilin provinces. This study is great important for understanding the law of urban construction land expansion in China, the regional differences in land urbanization level and the optimisation of land use layout.

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“…Among them, Defense Meteorological Satellite Program/ Operational Linescan System, DMSP/OLS, visible infrared imaging radiometer suite day/night band (VIIRS/DNB), and Luojia1-01(LJ1-01) have been widely studied and applied due to the convenience of image acquisition and wide coverage (Cheng et al, 2016;Elvidge et al, 2017;Li et al, 2019). However, most of the current studies on these three kinds of satellite images in fisheries focus on a single data type or a single area (Waluda et al, 2004;Wang et al, 2020;Zhong et al, 2020), without systematically sorting out the differences between the three kinds of night light remote sensing data with regard to the image quality values and lit fishing vessel identification effect, which results in issues with fishing vessel extraction and management.…”

Section: Introductionmentioning

confidence: 99%

Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

Zhou

Cheng

2023

Front. Environ. Sci.

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Fisheries provide high-quality protein for many people, and their sustainable use is of global concern. Light trapping is a widely used fishing method that takes advantage of the phototropism of fish. Remote sensing technology allows for the monitoring of lit fishing vessels at sea from the air at night, which supports the sustainable management of fisheries. To investigate the potential of different nighttime light remote sensing data for lit fishing vessel identification and applications, we used the fuzzy evaluation method to quantitatively assess images in terms of their radiometric and geometric quality, and Otsu’s method to compare the effects of lit fishing vessel identification. Three kinds of nighttime lighting data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), Visible infrared imaging radiometer suite day/night band (VIIRS/DNB), and Luojia1-01(LJ1-01) were analyzed, compared, and application pointers were constructed. The results are as follows. ①In the image radiation quality evaluation, the information entropy, clarity, and noise performance of the LJ1-01 image are higher than those of the DMSP/OLS and VIIRS/DNB images, where the information entropy value of the LJ1-01 image is nearly 10 times that of VIIRS/DNB and 23 times that of DMSP/OLS. The average gradient value is 14 times that of the image from VIIRS/DNB and 1,600 times that of DMSP/OLS, while its noise is only about 2/3 of the VIIRS/DNB image and 1/3 of the DMSP/OLS image. In the geometric quality assessment, the geometric positioning accuracy and ground sampling accuracy of the VIIRS/DNB image is the best among the three images, with a relative difference percentage of 100.1%, and the LJ1-01 and DMSP/OLS images are relatively lower, at 96.9% and 92.3%, respectively. ② The detection of squid fishing vessels in the Northwest Pacific is taken as an example to compare the identification effects of three types of data: DMSP/OLS, VIIRS/DNB, and LJ1-01. Among these data, DMSP/OLS can effectively identify the position of the lit fishing boat, and VIIRS/DNB images can accurately estimate the spatial position and number of lit fishing boats with large distances. However, in the case of fishing boats gathering or clustering, the number of fishing vessels could not be identified. This led to the detected number of lit fishing vessels being less than the real value. For the VIIRS/DNB and LJ1-01 images with a 5′×8′ span in the same spatiotemporal range using the same batch of pelagic squid fishing vessels, LJ1-01 extracted 18 fishing vessels. VIIRS/DNB extracted 15, indicating that LJ1-01 can distinguish multiple fishing vessels in the lighted overlapping area, thus accurately identifying the number of fishing vessels. The application pointing table generated based on the results of the three data analyses can provide a reference for sensor/image selection for nighttime light remote sensing fishery applications and a basis for more refined fishing vessel identification, extraction, and monitoring.

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Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

Cited by 16 publications

References 55 publications

Vessel Detection with SDGSAT-1 Nighttime Light Images

Vessel Detection with SDGSAT-1 Nighttime Light Images

Analysis of Spatiotemporal Characteristics and Influencing Factors of Land Urbanization Level in China at Different Scales Based on Nighttime Light Remote Sensing

Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

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