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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25477

Title: Detecting Surface Kuroshio Front in the Luzon Strait From Multichannel Satellite Data Using Neural Networks
Authors: Ruo-Shan Tseng;Chung-Ru Ho;Yung-Hsiang Lee;Quanan Zheng
Contributors: 國立臺灣海洋大學:海洋環境資訊系
Keywords: 黑潮入侵;類神經網路;遙感探測;水團
Date: 2010-10
Issue Date: 2011-10-20T08:23:21Z
Publisher: Geoscience and Remote Sensing Letters, IEEE
Abstract: abstract:An objective classification method is developed to distinguish the water masses of Kuroshio and South China Sea (SCS) by using an artificial neural network (ANN). Sea surface temperature (SST) and ocean-color data obtained from the Moderate Resolution Imaging Spectroradiometer in two specified areas to the east and west of Luzon, representing the Kuroshio and SCS waters, respectively, are used to train, validate, and test the ANN model. The water masses of Kuroshio and SCS can be distinguished correctly with a high success rate of over 99%. The model is then applied to the Luzon Strait, and the result of water mass classification agrees well with the temperature-salinity characteristics derived from a cruise in May and June of 2006. The performance is good in summertime when the SST or ocean color has a rather uniform spatial distribution and the traditional method of front detection by using a threshold value is inappropriate.
Relation: 7(4), pp.718-722
URI: http://ntour.ntou.edu.tw/handle/987654321/25477
Appears in Collections:[海洋環境資訊系] 期刊論文

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