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

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-pr...

Full description

Bibliographic Details
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
Description
Summary: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.

Similar Items