TY - GEN
T1 - Superpixel Based Sea Ice Segmentation with High-Resolution Optical Images
T2 - 10th International Conference on Communications, Signal Processing, and Systems, CSPS 2021
AU - Chen, Siyuan
AU - Yan, Yijun
AU - Ren, Jinchang
AU - Hwang, Byongjun
AU - Marshall, Stephen
AU - Durrani, Tariq
N1 - Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Satellite remote sensing
KW - Sea ice segmentation
KW - Superpixel
UR - http://www.scopus.com/inward/record.url?scp=85128785136&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-0390-8_126
DO - 10.1007/978-981-19-0390-8_126
M3 - Conference contribution
AN - SCOPUS:85128785136
SN - 9789811903892 (hbk)
SN - 9789811903922 (pbk)
T3 - Lecture Notes in Electrical Engineering
SP - 1004
EP - 1012
BT - Communications, Signal Processing, and Systems
A2 - Liang, Qilian
A2 - Wang, Wei
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Zhang, Baoju
PB - Springer
CY - Singapore
Y2 - 24 July 2021 through 25 July 2021
ER -