Sea Ice Type Classification with Optical Remote Sensing Data
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- Sea Ice Type Classification with Optical Remote Sensing Data
- Other Titles
- 광학영상에서의 해빙종류 분류 연구
- Chi, Junhwa
- Science & Technology - Other Topics
- Active learning; Convolutional neural network; Deep learning; Sea ice; Semantic segmentation; Semi-supervised learning
- Issue Date
- Chi, Junhwa, Kim, Hyun-cheol. 2018. "Sea Ice Type Classification with Optical Remote Sensing Data". Korean Journal of Remote Sensing, 34(6-2): 1239-1249.
- Optical remote sensing sensors provide visually more familiar images than radar images.
However, it is difficult to discriminate sea ice types in optical images using spectral information based
machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation
which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical
and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised
and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve
the performance of supervised classification for multiple images. Therefore, we successfully added new
labels from unlabeled data to automatically update the semantic segmentation model. This should be
noted that an operational system to generate ice type products from optical remote sensing data may be
possible in the near future.
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