Machine learning techniques for sea-ice identification and classification ...
Sea-ice identification and classification are essential processes for safety critical navigation support of surface vessels in polar waters. Semantic segmentation has drawn much attention as an enabling technique for fast detection of objects in a scene including sea-ice conditions. Identifying sea-...
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| Format: | Text |
| Language: | English |
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Memorial University of Newfoundland
2023
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| Online Access: | https://dx.doi.org/10.48336/zvar-h250 https://research.library.mun.ca/16191/ |
| Summary: | Sea-ice identification and classification are essential processes for safety critical navigation support of surface vessels in polar waters. Semantic segmentation has drawn much attention as an enabling technique for fast detection of objects in a scene including sea-ice conditions. Identifying sea-ice is a challenging problem, especially in the presence of raindrops. The raindrop alters the boundaries of the objects in the scene, and thus, degrades the identification performance. In this work, deep-learning (DL) semantic segmentation networks are trained to classify the scene of sea-ice images including the VGG-16, fully convolutional network, pyramid scene parsing network, and conditional generative adversarial network (cGAN) semantic segmentation model. Two datasets are utilized to train the cGAN model. The images in the first dataset capture four classes: sea-ice, open water, sky, and vessel. The images in the second dataset capture first year sea-ice, new sea-ice, and gray sea-ice in addition to the ... |
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