Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images
Chai, Yanmei and Ren, Jinchang and Hwang, Byongjun and Wang, Jian and Fan, Dan and Yan, Yijun and Zhu, Shiwei (2020) Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14. pp. 577-586. ISSN 1939-1404 (https://doi.org/10.1109/JSTARS.2020.3040614)
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Abstract
Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods suffer from noise interference and mixture of water and ice caused high segmentation error and less robustness. In this study, we propose a novel sea ice floe segmentation algorithm from HRO images based on texture-sensitive superpixeling and twostage thresholding. First, sparse components are extracted from the HRO images using the Robust Principal Component Analysis (RPCA), and noise is removed by the bilateral filter. The enhanced image is obtained by combining the low-rank matrix and the sparse components. Second, a texture-sensitive Simple Linear Iterative Clustering (SLIC) superpixel algorithm is introduced for pre-segmentation of the enhanced HRO image. Third, a learning based adaptive thresholding in the two-stages is employed to generate the refined segmentation from the derived superpixels blocks. The efficacy of the proposed method is validated on two HRO images using visual assessment, quantitative evaluation (with seven metrics) and histogram comparison. The superior performance of the proposed method has demonstrated its efficacy for sea ice floe segmentation.
ORCID iDs
Chai, Yanmei, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Hwang, Byongjun, Wang, Jian, Fan, Dan, Yan, Yijun ORCID: https://orcid.org/0000-0003-0224-0078 and Zhu, Shiwei;-
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Item type: Article ID code: 74994 Dates: DateEvent25 November 2020Published25 November 2020Published Online23 November 2020AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Technology and Innovation Centre > Sensors and Asset Management
Faculty of Engineering > Electronic and Electrical EngineeringDepositing user: Pure Administrator Date deposited: 07 Jan 2021 14:10 Last modified: 02 Oct 2024 06:34 URI: https://strathprints.strath.ac.uk/id/eprint/74994