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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/25455

Title: Satellite chlorophyll retrievals with a bipartite artificial neural network model
Authors: Su FC;Ho CR;Zheng Q;Kuo NJ;Chen CT
Contributors: NTOU:Department of Marine Environmental Informatics
Date: 2006
Issue Date: 2011-10-20T08:23:17Z
Publisher: International Journal of Remote Sensing
Abstract: abstract:An artificial neural network (ANN) model with a bipartite classification scheme is developed to retrieve the chlorophyll‐a concentration (Chl) from sea‐viewing wide field‐of‐view sensor (SeaWiFS) data. Bio‐optical data derived from the SeaWiFS bio‐optical algorithm mini‐workshop (SeaBAM) are used to verify this bipartite artificial neural network (BANN) model. In comparison with SeaWiFS operational algorithms and a general ANN model, the BANN model significantly increases the accuracy of Chl retrieval not only on a log scale but also on a normal scale. The BANN model can significantly improve the accuracy of Chl especially in the high Chl region. The model also performs well in a test with in situ measurements from Taiwan coastal waters. The biases induced by errors in atmospheric correction are also reduced in the coastal water case.
Relation: 27(8), pp.1563-1579
URI: http://ntour.ntou.edu.tw/handle/987654321/25455
Appears in Collections:[海洋環境資訊系] 期刊論文

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