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Effective SAR image segmentation and sea-ice floe distribution analysis via kernel graph cuts based feature extraction and fusion

机译:通过基于核图割的特征提取和融合有效的SAR图像分割和海冰絮状分布分析

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摘要

The Sea Ice that grows in the open seas like the Arctic sea, forms varying shapes and size due to the fracturing as well as thickening caused by the strong gale force winds and sea waves. Over the winter season, due to the cooler temperature, these sea-ice regions combine with each other to make a stronger and larger sea ice block. In the summer however, due to the higher temperature, they separate into smaller and weaker floes. Sea-Ice monitoring has gained significant interest in recent years, largely due to the fact of the decreasing area and thickness of the older arctic sea ice (Kwok, et al., 2009) (Stroeve, et al., 2008). This decline in older sea ice has been linked largely to the growth of younger, thinner sea ice regions (Maslanik, et al., 2007) and also climate changes (Holloway & Sou, 2002), caused by greenhouse gases (Serreze, et al., 2007). The study of Polar Regions using Synthetic Aperture Radar [SAR] has been widely used for identification of sea ice floes, their size and their distribution (Burns, et al., 1987) (Rothrock & Thorndike, 1984), (Soh, et al., 2004), (Soh & Tsatsoulis, 1998). This is because SAR is not majorly affected by the harsh weather conditions or the illumination variations and it is able to cover large and primarly inaccesible areas (Xu, et al., 2014). This is particularly important for ensuring safe marine navigation as well as supporting studies of climate changes, like ours, of the Polar Regions. To date, the process of developing an automatic algorithm for effective segmentation of SAR Sea-Ice images has not been achievable. As a result, analysis of sea ice images relies on a time consuming expert analysis which is performed manually. For this reason, it is primarily important to develop techniques to automatically segment the sea-ice regions from the background and subsequently extract these sea-ice regions from the SAR image. When this is completed it will become possible to build a Floe Size Distribution (FSD) database, where FSD is a measure of the distribution of the different size of the sea ice floes. An FSD database will be constructed in our project by extracting and storing the total pixel area of these individual sea-ice regions in the SAR image and grouping them according to their size. The result will then be used to generate a graph of the size distribution of the floes on different days in a year of the Arctic region. The outcome of our study will further develop scientist’s understanding of the different trends as well as the various conditions affecting the size of the Arctic sea ice floes for that particular year. Eventually this will improve our understanding of the changes in the sea-ice extent over the year by means of comparison with the past several years’ results. The key research problem addressed by this work lies in developing a new novel image segmentation technique which is simple, fast and robust when used to segment the SAR sea ice images.
机译:在像北极海这样的公海中生长的海冰,由于强烈的烈风和海浪引起的破裂以及增厚,形成了各种形状和大小。在冬季,由于温度较低,这些海冰区域相互结合以形成更强壮的海冰块。但是,在夏季,由于温度较高,它们会分成较小和较弱的絮凝物。近年来,海冰监测引起了人们的极大兴趣,这主要是由于较老的北极海冰的面积和厚度减小了(Kwok等,2009)(Stroeve等,2008)。较老的海冰的下降主要与较年轻,较薄的海冰区域的生长有关(Maslanik等,2007),还与温室气体引起的气候变化(Holloway&Sou,2002)有关(Serreze等,2002)。 (2007年)。利用合成孔径雷达[SAR]研究极地地区已被广泛用于识别海冰的浮冰,其大小和分布(Burns等,1987)(Rothrock&Thorndike,1984),(Soh等,1984)。 (2004年),(Soh和Tsatsoulis,1998年)。这是因为SAR不受恶劣天气条件或光照变化的主要影响,并且能够覆盖较大且基本上不可达的区域(Xu等,2014)。这对于确保安全的海上航行以及支持极地地区像我们这样的气候变化研究特别重要。迄今为止,开发用于有效分割SAR Sea-Ice图像的自动算法的过程尚未实现。结果,对海冰图像的分析依赖于手动执行的耗时的专家分析。因此,开发自动从背景中分割海冰区域并随后从SAR图像中提取这些海冰区域的技术至关重要。完成此操作后,将有可能建立浮点尺寸分布(FSD)数据库,其中FSD是对不同大小的海浮冰分布的度量。在我们的项目中,将通过提取并存储SAR图像中这些单个海冰区域的总像素面积并根据其大小对其进行分组来构建FSD数据库。然后将结果用于生成北极地区一年中不同日期的絮凝物尺寸分布图。我们的研究结果将进一步发展科学家对特定年份不同趋势以及影响北极海冰浮冰大小的各种条件的理解。最终,通过与过去几年的结果进行比较,这将增进我们对一年中海冰范围变化的了解。这项工作解决的关键研究问题在于开发一种新颖的新颖图像分割技术,该技术在分割SAR海冰图像时非常简单,快速且可靠。

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