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Automatic coral island segmentation based on region-based multi-phase level set method: A case study on Pattle Island, South China Sea

机译:基于区域多相水平集方法的珊瑚岛自动分割-以南海帕特岛为例

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Remote sensing provides an effective way to observe and monitor coral islands with shallow coral reefs worldwide, to characterize inter-reef structural differences, and to map intra-reef zonation. Nowadays, although types of high and moderate resolution remote sensing data become various, the existing feature extraction methods are mainly confined in data-driven classification field. To maintain spatial-continuity of features, this paper applies a model-driven, multi-phase level set method (MPLSM) for specific circle-structured coral island segmentation based on CBERS-02B CCD multi-spectral image. The fundament of multi-phase level set method is to establish local cluster criterions to control level set evolution. Experiment shows that: (1) MPLSM segmentation avoids topology error and contour omission problems compared to classic Chan-Vese piece constant model; (2) MPLSM is robust to contour initialization so as to improve level set automation; (3) MPLSM cannot handle with the diffusive boundary and areas seriously influenced by mixed-pixel effect, but can ensure level set convergence.
机译:遥感为观察和监测全世界浅珊瑚礁的珊瑚岛,表征珊瑚礁之间的结构差异以及绘制珊瑚礁内分区提供了一种有效的方法。如今,尽管高分辨率和中分辨率的遥感数据种类繁多,但现有的特征提取方法主要局限于数据驱动的分类领域。为了保持特征的空间连续性,本文基于CBERS-02B CCD多光谱图像,将模型驱动的多相水平集方法(MPLSM)用于特定的圆形结构珊瑚岛分割。多阶段水平集方法的基础是建立局部聚类准则,以控制水平集的演化。实验表明:(1)与经典的Chan-Vese块常数模型相比,MPLSM分割避免了拓扑错误和轮廓遗漏问题; (2)MPLSM具有强大的轮廓初始化能力,可以提高水平集的自动化程度; (3)MPLSM不能处理扩散边界和受混合像素效应严重影响的区域,但可以确保水平集收敛。

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