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系統識別號 U0002-2707201110231500
中文論文名稱 應用類神經網路於衛星影像淹水辨識之研究
英文論文名稱 A Study of Flood Identification in Satellite Image Using Artificial Neural Networks
校院名稱 淡江大學
系所名稱(中) 水資源及環境工程學系碩士班
系所名稱(英) Department of Water Resources and Environmental Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 高毅灃
研究生英文姓名 I-Feng Kao
學號 698480265
學位類別 碩士
語文別 中文
口試日期 2011-07-04
論文頁數 62頁
口試委員 指導教授-張麗秋
委員-張斐章
委員-施國肱
委員-王藝峰
委員-張麗秋
中文關鍵字 倒傳遞類神經網路  淹水辨識  衛星影像  合成孔徑雷達 
英文關鍵字 back-propagation neural network  flood extent identification  satellite image  synthetic aperture radar 
學科別分類 學科別應用科學環境工程
中文摘要 臺灣地區易受到颱風暴雨侵襲,常發生水災,且因地形山高河短,淹水過程相當短暫,又受天氣影響,不適合使用飛機或由平流層上方的可見光衛星觀測完整災區,故最適合調查災區淹水區域之手段即為使用可穿透雲層之合成孔徑雷達(SAR)衛星。
本研究使用倒傳遞類神經網路(BPNN)模式及多變量線性迴歸(MLR)模式,結合SAR衛星影像資料,以建構淹水區域辨識模式。其中,BPNN模式可分為僅用淹水時SAR影像之模式一,以及使用淹水前及淹水時兩張SAR影像之模式二,輸入變數包含各像素點經轉換後之雷達散射截面(RCS)值、自身及其鄰近9宮格之統計平均值、標準差、最小值及最大值;MLR模式使用淹水前及淹水時兩張SAR影像,輸入變數兩張影像中各像素點之RCS值差異量、自身及其鄰近9宮格RCS值差異之統計變異數。
結果顯示BPNN模式有較佳的辨識效果,訓練資料與測試資料之淹水辨識正確率分別高達80%與73%以上。錯誤辨識區域大多為分佈零散、未集中於特定區域;為修正這些小而零散的區域,使用型態影像學運算處理將模式輸出結果進行修正,修正後結果正確率大為提升,辨識正確率可提升至90%以上。
英文摘要 Typhoons and storms hit Taiwan several times each year and they cause serious
flood disasters. The rivers are short and steep, and their flows are relatively quick
with floods lasting only few hours. Due to the factors of the weather, it is not suitable
for aircraft or traditional multispectral satellite; hence, the most appropriate way for
investigating flood extent is to use Synthetic Aperture Radar (SAR) satellite.
In this study, back-propagation neural network (BPNN) model and multivariate
linear regression (MLR) model are constructed to identify the flood extent from SAR
satellite images. The input variables of the BPNN model are the pixel’s Radar Cross
Section (RCS) value and mean, standard deviation, minimum and maximum of RCS
values among its adjacent 3×3 pixels. The MLR model uses two images, including
the flooding before and the input variables of the MLR model are the difference
between the RCS values of two images and the variances among its adjacent 3×3
pixels.
The results show that the BPNN model can perform much better than the MLR
model. The correct percentages are more than 80% and 73% in training and testing
data, respectively. However, the locations of many misidentified areas are very
fragmented and unrelated. For correcting the small and fragmented areas,
morphological operations are used to modify the outputs of these three identification
models. The modified results have been improved a lot and the correct percentages
increase up to 90%.
論文目次 目錄
目錄.................................................I
表目錄...............................................III
圖目錄...............................................IV
一、前言.............................................1
1.1 研究動機與目的...................................1
1.2 研究方法.........................................2
二、文獻回顧.........................................3
2.1 應用類神經網路進行影像辨識.......................3
2.2應用類神經網路進行遙測影像分類....................3
2.3應用合成孔徑雷達衛星辨識水體或淹水區..............4
三、理論概述.........................................6
3.1 類神經網路.......................................6
3.1.1 倒傳遞類神經網路...............................8
3.1.2 誤差倒傳遞演算法...............................9
3.2衛星遙測概述......................................12
3.2.1合成孔徑雷達衛星................................13
3.2.2合成孔徑雷達衛星影像............................14
四、研究案例.........................................16
4.1 研究區域概況.....................................16
4.2 資料蒐集與處理...................................18
4.2.1 衛星影像處理...................................18
4.2.2 淹水區域調查...................................22
4.3評估指標..........................................26
4.4 模式架構.........................................28
4.5 淹水辨識模式.....................................33
4.6 綜合討論.........................................42
4.7以型態影像修正模式辨識結果........................45
4.8 宜蘭縣其他地區淹水區域辨識.......................48
五、結論與建議.......................................51
5.1 結論.............................................51
5.2建議..............................................52
六、參考文獻.........................................53
附錄.................................................60


表目錄
表3.1 淹水區之水面及空地於不同偏極上的反射情形對照表.14
表4.2 本研究使用之SAR影像系統參數....................18
表4.3 誤差矩陣.......................................27
表4.4 類神經網路淹水辨識模式輸入變數組合.............29
表4.5模式一不同輸入變數組合之辨識結果................33
表4.6 模式一最佳輸入變數組合之誤差矩陣...............34
表4.7模式二不同輸入變數組合之辨識結果................36
表4.8 模式二最佳輸入變數組合之誤差矩陣...............37
表4.9模式三不同淹水門檻值之辨識結果..................39
表4.10 模式三最佳淹水門檻值之誤差矩陣................40
表4.11 各模式訓練與測試之結果比較表..................42
表4.12 各模式訓練及測試之誤差矩陣比較表..............43
表4.13 各模式訓練與測試之結果比較表..................46
附表1 ALOS衛星SAR影像拍攝方式與感測器規格............60
附表2 ALOS衛星SAR影像各等級產品規格表................62

圖目錄
圖3.1 BPNN架構圖.....................................8
圖3.2 合成孔徑雷達意識圖.............................13
圖3.3 SAR影像與地表物體之反射情形....................15
圖4.1得子口溪水系地理位置與研究區域相對位置圖........17
圖4.2淹水前SAR衛星影像(a)HH頻道,(b)HV頻道.......19
圖4.3淹水時SAR衛星影像...............................20
圖4.4 縣府調查淹水區.................................22
圖4.5 現地調查淹水區.................................23
圖4.6 模式所使用之淹水區及非淹水區範本...............24
圖4.7 訓練與測試區之分割圖...........................25
圖4.8 模式一架構圖...................................30
圖4.9 模式二架構圖...................................31
圖4.10 模式一淹水辨識結果............................35
圖4.11 模式二淹水辨識結果............................38
圖4.12 模式三淹水辨識結果............................41
圖4.13 模式一與模式二辨識結果之差異..................43
圖4.14淹水前與淹水時SAR影像差異分布圖................44
圖4.15 模式一修正後淹水辨識結果......................46
圖4.16 模式二修正後淹水辨識結果......................47
圖4.17 模式三修正後淹水辨識結果......................47
圖4.18 壯圍鄉新社村及古亭村之可見光影像原圖..........49
圖4.19 模式二於壯圍鄉新社村及古亭村之淹水區域推估....49
圖4.20 三星鄉尾塹村之可見光影像圖....................50
圖4.21模式二於三星鄉尾塹村之淹水區域推估結果.........50
附圖1 ALOS衛星SAR影像拍攝方式意識圖..................61
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