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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/53872


    Title: 應用RGB影像處理模式於辨識地理環境變異之研究;Developing a RGB recognition model for the disaster caused by Typhoon Morakot in Taiwan
    Authors: 李樹璇;Lee,Shu-Xuan
    Contributors: 營建管理研究所
    Keywords: 遙測影像;變異監測;影像處理;remote sensing imagery;typhoon disaster.;RGB damage assessment
    Date: 2012-07-25
    Issue Date: 2012-09-11 18:18:06 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本研究之目的在於利用具有即時性及周期性之遙測影像判釋橋梁周遭地區如崩塌地、林地、河道、河道寬度因災損而造成的改變,且針對此部分開發一個影像辨識系統以供使用,用以判斷災損嚴重程度。本研究利用國立中央大學太空及遙測研究中心遙測影像訂購系統訂購八八風災災前災後之福衛二號遙測影像,利用設定橋梁經緯度座標挑選訂購橋梁遙測影像,其中包含設定日期範圍及過濾含雲量過高之遙測影像,使用災前災後遙測影像Pixel點之像素點RGB變化作為判釋主要依據同時進行變異監測,以C++為基礎開發一套影像判釋系統主要能對RGB變化判釋及包含各種影像處理功能如label、dialation、erosion、bone、cutline等影像處理功能加強對於遙測影像上需要處理的部分,根據RGB影像處理模式得出之結果進行處理則可得知河道骨幹及平均寬度,能夠清楚的表明災前災後遙測影像的變化及變化程度,並利用災前災後河道骨幹套疊了解河道改道狀況。根據所得之結果能夠求得受災地區之面積變化、受災河道寬度之變化、何處受災程度嚴重、河道改道靠近鄰近村落情形,以此資訊幫助受損橋梁周遭災害範圍辨識,並利用Google SketchUP進行辨識範圍準確度驗證,準確度驗證結果有89%以上。Typhoon Morakot has been the most severe typhoon disaster to strike Taiwan in recent decades causing tremendous damage to bridge surroundings in 2009. However, we still lack a means of assessing post-typhoon damage for follow-up rebuilding. This paper presents an integrated model that automatically measures changes in rivers, areas of damage to bridge surroundings, and changes in vegetation. The proposed model is based on a RGB enanced by the SOM optimization algorithm, and also includes the particular functions of dilation, erosion, and skeletonization to deal with river imagery. High resolution FORMOSAT-2 satellite imagery from before and after the invasion period is adopted. A bridge is randomly selected from the 129 destroyed due to the typhoon for applications of the model. The recognition results show that the river average width has increased 66% with a maximum increase of over 200%. The ruined segment of the bridge is located exactly in the most scoured region. There has also been a nearly 10% reduction in the vegetation coverage. The results yielded by the proposed model demonstrate a pinpoint accuracy rate of 99.94%. This study successfully develops a tool for large-scale damage assessment as well as for precise measurement after disasters.
    Appears in Collections:[營建管理研究所 ] 博碩士論文

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