Superpixel based sea ice segmentation with high-resolution optical images: analysis and evaluation.

• Main Authors: Chen, Siyuan, Yan, Yijun, Ren, Jinchang, Hwang, Byongjun, Marshall, Stephen, Durrani, Tariq
• Other Authors: Liang, Qilian, Wang, Wei, Liu, Xin, Na, Zhenyu, Zhang, Baoju
• Format: Conference Object
• Language: unknown
• Published: Springer 2022
• Subjects: Sea ice segmentation; Superpixel; Satellite remote sensing; Chukchi Sea; Chukchi; Sea ice
• Online Access: https://doi.org/10.1007/978-981-19-0390-8_126; https://rgu-repository.worktribe.com/file/1654079/1/CHEN%202022%20Superpixel%20based%20sea%20%28AAM%29; https://rgu-repository.worktribe.com/output/1654079

By grouping pixels with visual coherence, superpixel algorithms provide an alternative representation of regular pixel grid for precise and efficient image segmentation. In this paper, a multi-stage model is used for sea ice segmentation from the high-resolution optical imagery, including the pre-processing to enhance the image contrast and suppress the noise, superpixel generation and classification, and post-processing to refine the segmented results. Four superpixel algorithms are evaluated within the framework, where the high-resolution imagery of the Chukchi sea is used for validation. Quantitative evaluation in terms of the segmentation quality and floe size distribution, and visual comparison for several selected regions of interest are presented. Overall, the model with TS-SLIC yields the best results, with a segmentation accuracy of 98.19% on average and adhering to the ice edges well.