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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
Kim, Hyun-cheol
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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