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研究生:蘇蜂鈞
研究生(外文):Feng-Chiung Su
論文名稱:類神經網路應用於海洋生光模式
論文名稱(外文):Applications of Artificial Neural Network on Oceanic Bio-Optical Algorithm
指導教授:何宗儒何宗儒引用關係
指導教授(外文):Chung-Ru Ho
學位類別:碩士
校院名稱:國立海洋大學
系所名稱:海洋科學系
學門:自然科學學門
學類:海洋科學學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:80
中文關鍵詞:葉綠素類神經網路生光模式輻射量
外文關鍵詞:chlorophyllneural networkbio-opticalradiance
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傳統的生物光學演算法使用迴歸方法,利用輻射量求取葉綠素濃度,如海洋廣角感測器(簡稱SeaWiFS)所使用的OC2演算法。本篇論文利用倒傳遞網路演算法,建立海水中葉綠素濃度與海水輻射量間的生物光學模式。使用類神經網路的優點為:1) 利用類神經網路的非線性解題能力能有效建立葉綠素濃度與輻射量間的轉換函數。2) 類神經網路使用所有可用的波段,而非傳統的方法僅使用兩個波段比值,如此可有效利用所有波段所包含的訊息。
本篇論文使用SeaWiFS生物光學演算會議(SeaWiFS Bio-optical Algorithm Mini-workshop, SeaBAM)所獲得的生物光學資料,發展並驗證類神經網路生物光學演算法。結果顯示使用類神經網路推算生物光學演算法時,其葉綠素濃度檢定係數為0.98,葉綠素濃度均方根誤差值為0.59 ug/l。此結果遠較傳統的OC2v2演算法準確,OC2v2的葉綠素濃度檢定係數為0.56,葉綠素濃度均方根誤差值為2.84 ug/l。

Traditional bio-optical algorithm uses regression method to derive chlorophyll concentration form radiance, such as the OC2 algorithm for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). In this study, a back propagation neural network is employed to set up the bio-optical algorithm between chlorophyll concentration in the oceans and radiance from the oceans. The advantages of the neural network algorithm are 1) the ability for solving the non-linear transfer function between chlorophyll concentration and radiance is better than that of regression method. 2) it uses the information of all bands instead of the two-band ratio of traditional algorithm.
Bio-optical data from SeaWiFS Bio-optical Algorithm Mini-workshop (SeaBAM) were used to develop and verify the neural network algorithm. The result show that the coefficient of determination ( R ) for the neural network algorithm is 0.98 and the root-mean-square (RMS) error of derived chlorophyll concentration is 0.59 ug/l. They are much better than the results of version 2 of OC2 (OC2v2) algorithm, while the R is 0.56 and the RMS error is 2.84 ug/l.

中文摘要 Ⅰ
英文摘要 Ⅱ
誌謝 Ⅲ
目錄 Ⅳ
表目錄 Ⅵ
圖目錄 Ⅶ
第一章 導論 1
1.1 研究動機 1
1.2 文獻回顧 3
1.2.1 海洋水色 3
1.2.2 類神經網路 7
1.3 論文大綱 8
第二章 理論與方法 9
2.1 類神經網路 9
2.1.1 類神經網路原理 9
2.1.2 生物神經學說 9
2.1.3 常用之非線性轉換函數 11
2.1.4 類神經網路之運算過程 13
2.1.5 倒傳遞網路演算摘理論推導 14
2.2 海洋水色生光模式演算法 17
2.1.1 遙測返照率 17
2.1.2 離水輻射量 19
2.3 光譜值資料來源與規範 21
2.3.1 SeaBAM篩選資料原則 21
2.3.2 SeaBAM最佳葉綠素濃度推算模式 23
第三章 神經網路應用於海洋生光模式 29
3.1 神經網路初試 31
3.2 迴歸公式的誤差 42
3.3 資料的選取 50
3.4 類神經網路生光模式 64
第四章 結論與建議 75
參考文獻 77

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