本文關鍵詞： 面向對象 高分辨率遙感 信息提取 滑坡 變化檢測 eCognition　出處：《蘭州大學》2015年碩士論文　論文類型：學位論文

【摘要】：2008年汶川“5.12”地震不僅影響了四川省大部分地區(qū),也對甘肅南部地區(qū)產(chǎn)生了很大影響,表現(xiàn)為由地震引發(fā)的滑坡、泥石流、崩塌等次生地質災害急劇增多,亟需對地質災害頻發(fā)的甘肅南部地區(qū)進行滑坡災害調查,為做好滑坡的防御減災工作奠定基礎。為了能夠第一時間獲取滑坡的相關災害信息,如滑坡的性質、規(guī)模、分布、對橋梁、道路、建筑物等的影響,提出用高分辨率遙感數(shù)據(jù)使用面向對象的方法進行滑坡體災害信息的提取。面向對象分析技術已在遙感領域的信息提取方面應用廣泛,區(qū)別于基于像元的方法,分類過程更加符合人類認知原理,用于分類的特征不僅包含光譜信息,還包含了紋理形狀、空間結構、上下文語義、拓撲關系等信息,避免了分類結果過于破碎,減弱了“椒鹽”噪聲的影響,分類精度有了明顯的改善。選用2007年11月IKONOS數(shù)據(jù)(全色波段的分辨率為lm)和2013年4月QuickBird影像(全色波段的分辨率為0.61m)兩個時相的高分遙感數(shù)據(jù)作為滑坡災害信息提取的遙感數(shù)據(jù)源,結合甘肅隴南地區(qū)的分辨率為30m的數(shù)字高程模型(DEM),采用易康軟件的面向對象分析方法,以分布有大量滑坡的甘肅南部地區(qū)為研究區(qū),進行滑坡災害信息的半自動提取研究。主要結論如下：(1)采用多尺度分割算法分別對建筑物、道路、植被和滑坡體設置不同的分割參數(shù)進行多次分割實驗,得到最優(yōu)分割參數(shù)并分析遙感數(shù)據(jù)的光譜特征、形狀特征、紋理特征等信息,得出最優(yōu)特征空間。(2)利用震前震后兩個時相的遙感數(shù)據(jù)獲得的建筑物信息提取結果進行變化檢測得到建筑物的變化信息,間接反映地質災害引發(fā)的建筑物損毀情況。(3)根據(jù)震后遙感數(shù)據(jù)受滑坡威脅的道路和植被的屬性特征,利用面向對象模糊分類法和閾值分類法建立規(guī)則級進行信息提取,為滑坡易損性快速評估提供技術支持。(4)采用面向對象支持向量機方法,選取合適的光譜、形狀、紋理等特征建立特征空間,通過徑向基核函數(shù)對選定的200個滑坡樣本和300個非滑坡樣本進行機器學習訓練,最終獲得滑坡體提取結果。對滑坡災害信息的各提取結果選用混淆矩陣定量評價方法進行精度評價,滑坡災害信息的提取總精度達到85%以上。綜上所述,面向對象的分類方法適用于滑坡災害信息提取,提取結果與真實情況相符,也滿足實際應用需求,為滑坡災情及承災信息的迅速評估提供依據(jù)。
[Abstract]:The 2008 Wenchuan "5.12" earthquake not only affected most areas of Sichuan Province, but also had a great impact on the southern part of Gansu Province, which was manifested by landslides and debris flows caused by the earthquake. The number of secondary geological disasters such as collapses is increasing rapidly, and it is urgent to investigate the landslide disasters in the southern part of Gansu, where geological disasters occur frequently. In order to be able to obtain the landslide disaster information in the first time, such as the nature, scale, distribution of landslides, the impact on bridges, roads, buildings and so on. An object oriented method is proposed to extract landslide disaster information using high resolution remote sensing data. Object-Oriented Analysis (OOA) technology has been widely used in the field of remote sensing information extraction, which is different from the pixel based method. The classification process is more in line with human cognitive principles. The features used for classification not only contain spectral information, but also contain texture, spatial structure, context semantics, topology and other information. The classification results are not too broken and the effect of "salt and pepper" noise is weakened. The classification accuracy has been significantly improved. The IKONOS data of November 2007 (resolution in panchromatic band) and QuickBird images of April 2013 (with a resolution of lm) were selected. The resolution of panchromatic band is 0.61m) the high-score remote sensing data of two phases are used as the remote sensing data source of landslide disaster information extraction. Combined with the digital elevation model with a resolution of 30 m in Longnan region of Gansu Province, the object oriented analysis method of Yikang software is adopted, and the study area is the southern part of Gansu, where a large number of landslides are distributed. Research on semi-automatic extraction of landslide disaster information. The main conclusions are as follows: (1) Multi-scale segmentation algorithm is used to set different segmentation parameters of buildings, roads, vegetation and landslide for many times. The optimal segmentation parameters are obtained and the spectral features, shape features, texture features of remote sensing data are analyzed. Get the optimal feature space. 2) using the remote sensing data of the two phases before and after the earthquake to extract the building information to detect the change information of the building. Indirectly reflects the geological hazards caused by the damage of buildings. 3) according to the remote sensing data after the earthquake landslide threatened by the road and vegetation attributes. Object oriented fuzzy classification and threshold classification are used to establish rule level for information extraction, which provides technical support for rapid assessment of landslide vulnerability. (4) Object-Oriented support Vector Machine (OSVM) method is used. Appropriate spectral, shape, texture and other features are selected to establish the feature space, and then 200 landslide samples and 300 non-landslide samples are trained by radial basis function (RBF). Finally, the results of landslide body extraction were obtained. The accuracy of landslide information extraction was evaluated by the method of confusion matrix quantitative evaluation. The total accuracy of landslide disaster information extraction was more than 85%. To sum up, the total accuracy of landslide disaster information extraction was more than 85%. The object-oriented classification method is suitable for the landslide disaster information extraction. The extraction results are consistent with the real situation and meet the practical application needs. It provides the basis for the rapid evaluation of landslide disaster situation and disaster information.
【學位授予單位】：蘭州大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P642.22;P237

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