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Image Processing for Identification of Sea-Ice Floes and the Floe Size Distributions

机译:识别海冰浮游物和浮游物大小分布的图像处理

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摘要

An unmanned aerial vehicle was used as a mobile sensor platform to collect sea-ice features at Ny-Ålesund in early May 2011, and several image processing algorithms have been applied to samples of sea-ice images to extract useful information about sea ice. The sea-ice statistics given by the floe size distribution, being an important parameter for climate and wave- and structure-ice analysis, is challenging to calculate due to difficulties in ice floe identification, particularly the separation of seemingly connected ice floes. In this paper, the gradient vector flow (GVF) snake algorithm is applied to solve this problem. To evolve the GVF snake algorithm automatically, an initialization based on the distance transform is proposed to detect individual ice floes, and the morphological cleaning is afterward applied to smoothen the shape of each identified ice floe. Based on the identification result, the image is separated into four different layers: ice floes, brash pieces, slush, and water. This makes it further possible to present a color map of the ice floes and brash pieces based on sizes, and the corresponding ice floe size distribution histogram. The proposed algorithm yields an acceptable identification result, and its effectiveness is demonstrated in a case study. A discussion on the methods and results concludes the paper.
机译:2011年5月上旬,无人驾驶飞机被用作移动传感器平台来收集Ny-Ålesund的海冰特征,并且几种图像处理算法已应用于海冰图像样本,以提取有关海冰的有用信息。浮冰大小分布给出的海冰统计数据是气候以及波浪和结构冰分析的重要参数,由于难以识别浮冰,尤其是看似相连的浮冰,因此很难进行计算。本文采用梯度矢量流(GVF)蛇形算法解决了该问题。为了自动演化GVF蛇算法,提出了一种基于距离变换的初始化来检测单个浮冰,然后进行形态学清洗以平滑每个识别出的浮冰的形状。根据识别结果,图像被分为四个不同的层:浮冰,bra片,雪泥和水。这使得进一步有可能基于大小以及相应的浮冰尺寸分布直方图显示浮冰和脆块的颜色图。该算法产生了令人满意的识别结果,并通过案例研究证明了其有效性。本文对方法和结果进行了讨论。

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