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    Title: 應用類神經網路於衛星影像淹水辨識之研究
    Other Titles: A study of flood identification in satellite image using artificial neural networks
    Authors: 高毅灃;Kao, I-Feng
    Contributors: 淡江大學水資源及環境工程學系碩士班
    張麗秋;Chang, Li-Chiu
    Keywords: 倒傳遞類神經網路;淹水辨識;衛星影像;合成孔徑雷達;back-propagation neural network;flood extent identification;satellite image;synthetic aperture radar
    Date: 2011
    Issue Date: 2011-12-28 19:25:07 (UTC+8)
    Abstract: 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。
    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%.
    Appears in Collections:[水資源及環境工程學系暨研究所] 學位論文

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