Sea ice detection from persistent single-channel shortwave infrared satellite data

Lewis, Nicholas S.; Koenig, L.; Grant, G.; Gallaher, D. W.; Schaefer, Kevin; Thompson, Jeff. E.; Campbell, G. G.

doi:10.1016/j.ecoinf.2019.05.013

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Many efficient frameworks have been proposed to enhance maritime traffic safety in polar regions, such as the global Polar Code [11]. Scholars employ multiple data sources (e.g., satellite images and ice model data) to obtain ship navigation environment information, which is used to fine-tune ship voyage plans [12,13]. Indeed, maritime traffic safety in polar regions can be significantly improved with the use of increasing amounts of weather and ice condition information [14,15].…”

Section: Introductionmentioning

confidence: 99%

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

Lin

Wang

Zhang

et al. 2021

Journal of Advanced Transportation

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The accurate design of ship routing plans in arctic areas is not easy, considering that navigation conditions (e.g., weather, visibility, and ice thickness) may change frequently. A ship’s crew identifies sea ice in arctic channels with the help of radar echoes, and ship maneuvering decisions are made to avoid navigation interference. Ship officials must manually and consistently change the ship’s route of travel, which is time-consuming and tedious. To address this issue, we propose a near-field route optimization model for the purpose of automatically selecting an optimal route with the help of radar echo images. The ship near-field route optimization model uses a multiobjective optimal strategy considering factors of minimum navigation risk and steaming distance. We verified the model’s performance with the support of the Xuelong voyage dataset. This research finding can help a ship’s crew to design more reasonable navigation routes in polar channels.

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Section: Introductionmentioning

confidence: 99%

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

Lin

Wang

Zhang

et al. 2021

Journal of Advanced Transportation

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Deep-learning-enhanced ice thickness measurement using Raman scattering

et al. 2019

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In ice thickness measurement (ICM) procedures based on Raman scattering, a key issue is the detection of ice–water interface using the slight difference between the Raman spectra of ice and water. To tackle this issue, we developed a new deep residual network (DRN) to cast this detection as an identification problem. Thus, the interface detection is converted to the prediction of the Raman spectra of ice and water. We enabled this process by designing a powerful DRN that was trained by a set of Raman spectral data, obtained in advance. In contrast to the state-of-the-art Gaussian fitting method (GFM), the proposed DRN enables ICM with a simple operation and low costs, as well as high accuracy and speed. Experimental results were collected to demonstrate the feasibility and effectiveness of the proposed DRN.

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Sea ice detection from persistent single-channel shortwave infrared satellite data

Cited by 2 publications

References 4 publications

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

Deep-learning-enhanced ice thickness measurement using Raman scattering

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