Identification of upwelling areas on sea surface temperature images using fuzzy clustering
Identification of upwelling areas on sea surface temperature images using fuzzy clustering
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DOI：
10.1016/j.rse.2008.01.014
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发表时间：
2008-06
影响因子：
13.5
通讯作者：
F. Sousa;Susana Nascimento;Hugo Casimiro;D. Boutov
中科院分区：
文献类型：
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作者：
F. Sousa;Susana Nascimento;Hugo Casimiro;D. Boutov
1992年9月至2003年9月期间在葡萄牙沿海获得的16个海表温度（SST）图像，旨在自动识别上升流沃茨所覆盖的区域。适当的高分辨率色标被应用到SST图像，以增强热模式，并容易地识别沿海涌升起源的沃茨。上升流沃茨所覆盖的区域的自动识别开发的作者在以前的工作中，通过应用模糊聚类和验证指数，这里是探索作为一个海洋学应用葡萄牙沿海上升流。模糊C-均值（FCM）算法表明，能够找到分区，密切定义的上升流区域和可视化的模糊C-分区是通过应用程序的色标。Xie-Beni验证指数被用来选择最能代表上升流事件阶段的c分区，并在这项工作中使用的14个SST分割图像中的10个中显示出与海洋学解释的一致性。两个SST图像没有上升流，也被用来检查的算法的响应的情况下的现象。计算的匹配率之间的c-分区和两个领域的分裂的手轮廓上升流边界也允许评估如何密切获得的分割再现上升流沃茨所覆盖的区域的形状。该方法成功地识别了14个SST图像中的10个的上升流边界区。所获得的匹配率的值高于0.77，从而指示模糊划分的良好质量。分割图像与3或4个集群是最合适的再现上升流沃茨覆盖的地区，但它也表明，在某些情况下，上升流地区可以合理地很好地再现FCM 2分区图像。而在后者中，上升流沃茨覆盖的区域与第一个集群相一致，在前者中，分割的图像显示了上升流区域内的两个集群：第一个集群与最近的上升流沃茨所占据的区域相一致，而第二个集群与“老”的上升流沃茨所占据的区域相一致，其延伸到近海，即所谓的冷丝。FCM算法显示是一种很有前途的技术在自动识别的上升流区的SST图像。
Sixteen sea surface temperature (SST) images obtained over the coastal ocean of Portugal during the period September 1992–September 2003 were used aiming to identify automatically the areas covered by upwelling waters. Suitable high resolution colour scales were applied to the SST images in order to enhance the thermal patterns and easily identify the waters with a coastal upwelling origin. The automatic identification of the areas covered by upwelling waters was developed by the authors in a previous work, through the application of fuzzy clustering and validation indexes, and here is explored as an oceanographic application to the Portuguese coastal upwelling. The fuzzy c-means (FCM) algorithm showed to be able to find partitions that closely defined the upwelling areas and the visualization of the fuzzy c-partitions was achieved through the application of a colour scale. The Xie-Beni validation index was used to select the c-partition that best represented the stage of the upwelling event and showed an agreement with the oceanographic interpretation in 10 of the 14 SST segmented images used in this work. Two SST images without upwelling were also used in order to check the response of the algorithm to the absence of the phenomena. The computation of the matching rate between a c-partition and the two areas split by the hand-contoured upwelling boundary also allowed the evaluation of how closely the obtained segmentation reproduced the shape of the areas covered by upwelling waters. This method successfully identified the upwelling boundary regions in 10 of the 14 SST images. The values obtained for the matching rate were higher than 0.77, thus indicating the good quality of the fuzzy partitions. The segmented images with 3 or 4 clusters were the most suitable ones to reproduce the areas covered by upwelling waters, but it was also shown that, for some cases, the upwelling areas could be reasonably well reproduced by the FCM 2-partition images. While in the latter, the area covered with upwelling waters was coincident with the first cluster, in the former, the segmented image showed two clusters within the upwelling area: the first cluster coincided with the area occupied by the most recently upwelled waters near the coast, while the second cluster was coincident with the area occupied by the “older” upwelling waters with its extensions offshore, the so-called cold filaments. The FCM algorithm revealed to be a promising technique in the automatic identification of upwelling areas on SST images.