Superpixel based sea ice segmentation with high-resolution optical images: analysis and evaluation.
Superpixel based sea ice segmentation with high-resolution optical images: analysis and evaluation.
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发表时间：
2022
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通讯作者：
Siyuan Chen;Yijun Yan;Jinchang Ren;Phil Hwang;Stephen Marshall;Tariq Durrani
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作者：
Siyuan Chen;Yijun Yan;Jinchang Ren;Phil Hwang;Stephen Marshall;Tariq Durrani
。通过对具有视觉一致性的像素进行分组，超像素算法为精确高效的图像分割提供了一种规则像素网格的替代表示。本文采用多阶段模型对高分辨率光学图像中的海冰进行分割，包括增强图像对比度和抑制噪声的前处理、超像素的生成和分类、以及后处理对分割结果的细化。在框架内评估了四种超像素算法，即SLIC、BASS、TS-SLIC和WP，其中使用楚科奇海的高分辨率图像进行了验证。总体而言，该模型的分割正确率平均为98.19%，并能很好地贴合冰层边缘。我们还根据分割质量和浮团大小分布进行了定量评估，并与选定的几个感兴趣区域进行了直观比较。结果发现，TS-SLIC在组内的性能最好。
. 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, i.e. SLIC, BASS, TS-SLIC and WP, where the high-resolution imagery of the Chukchi sea is used for validation. Overall, the model yields a segmentation accuracy of 98.19% on average and adhere the ice edges well. We also present quantitative evaluation in terms of the segmentation quality and floe size distribution, and visual comparison with several selected regions of interest. It is found that TS-SLIC performs the best within the group.