本文選題：不透水面　切入點：光譜混合分解　出處：《山東農業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：遙感影像解譯與信息提取一直是國際遙感領域研究的難點與熱點問題。城區(qū)土地覆蓋分類研究一直是全球學者研究現(xiàn)代地學關注的焦點與核心,此研究既可以為城市土地相關規(guī)劃、可持續(xù)發(fā)展相關政策的出臺提供重要參考,又可以為城市土地的合理利用提供數(shù)據(jù)基礎。不透水面是城區(qū)地表覆蓋重要的組分之一,不透水面覆蓋度是一個區(qū)域城鎮(zhèn)化程度、生態(tài)環(huán)境變化的重要指示因子。同時,不透水面的變化情況可以客觀反映一座城市的城市化和城市擴展情況。21世紀以來,遙感技術發(fā)展迅速,國內外科研人員利用遙感技術開展不透水面有關的研究與應用日漸增多,如在城市專題制圖、城市生態(tài)環(huán)境監(jiān)測等領域開展應用研究。獲取一座城市準確、可靠的不透水面覆蓋度信息可以為這些研究提供準確的輸入?yún)?shù)。本文主要圍繞基于中等空間分辨率遙感影像的亞像元不透水面覆蓋度估算、融合數(shù)字正射影像與nDSM數(shù)據(jù)的面向對象城區(qū)不透水面提取兩個主題開展研究。主要研究內容和結論如下:(1)中等空間分辨率衛(wèi)星影像不透水面覆蓋度估算方法。使用德國路德維希堡市2010年的中等空間分辨率Landsat 5衛(wèi)星影像,應用完全約束最小二乘混合像元分解方法進行亞像元級不透水面覆蓋度遙感估算研究。得到了德國路德維希堡市的不透水面覆蓋度情況。并應用研究區(qū)內高分辨率遙感影像對實驗結果進行了精度驗證,得到不透水面覆蓋度的估算值與真實值兩者的平均相對誤差為12.00%、相關系數(shù)為0.81,驗證了上述不透水面覆蓋度估算方法的可靠性。解決了傳統(tǒng)線性混合像元分解豐度圖經(jīng)常出現(xiàn)負值或者大于1的問題,解決了高分辨率遙感影像難以全部覆蓋研究區(qū)的問題。(2)面向對象的高分辨率遙感影像城區(qū)不透水面精細提取方法。以德國路德維希堡市為研究區(qū),融合分辨率為0.09m的數(shù)字正射影像與nDSM數(shù)據(jù),利用面向對象的影像分類方法對研究區(qū)進行了地表覆蓋分類。其中,同時使用了影像的光譜、紋理特征和nDSM的高程特征,分別使用支持向量機、隨機森林、規(guī)則分類、模糊隸屬度函數(shù)等分類器進行分類,并通過總體分類精度、Kappa系數(shù)等評定標準對分類結果的精度進行了客觀評價。實驗結果表明,支持向量機、隨機森林、模糊隸屬度函數(shù)分類、規(guī)則分類的總體分類精度分別為100.00%、99.05%、99.05%、91.43%,Kappa系數(shù)為1.0000、0.9871、0.9871、0.8840。
[Abstract]:Remote sensing image interpretation and information extraction has been a difficult and hot issue in the field of international remote sensing. The study of urban land cover classification has always been the focus and core of global scholars' research on modern geoscience. This study can not only provide an important reference for urban land related planning and sustainable development policies, but also provide a data basis for the rational use of urban land. Impermeable surface is an important component of urban land cover. Impermeable coverage is an important indicator of urbanization and ecological environment change in a region. At the same time, the change of impermeable surface can objectively reflect the urbanization and urban expansion of a city since the 21st century. With the rapid development of remote sensing technology, researchers at home and abroad use remote sensing technology to carry out research and application of impermeable surface, such as urban thematic mapping, urban ecological environment monitoring and other fields. Reliable coverage information of impermeable surface can provide accurate input parameters for these studies. This paper mainly focuses on the estimation of subpixel impermeable water coverage based on middle spatial resolution remote sensing images. In this paper, two subjects of object oriented urban impermeable surface extraction from digital orthophoto and nDSM data are studied. The main contents and conclusions are as follows: 1) estimation method of impermeability coverage of medium spatial resolution satellite images. Using a medium-resolution Landsat 5 satellite image from Ludwig, Germany, on 2010, In this paper, the method of fully constrained least square mixed pixel decomposition is used to study the remote sensing estimation of subpixel level impervious surface coverage. The case of impermeable coverage in Ludwig, Germany, is obtained, and the high resolution in the study area is applied. The accuracy of the experimental results is verified by the rate remote sensing image. The average relative error between the estimated value of impermeable surface coverage and the real value is 12.00 and the correlation coefficient is 0.81, which verifies the reliability of the above methods and solves the problem of traditional linear mixed pixel decomposition abundance. Graphs often have negative values or problems greater than 1, This paper solves the problem that high resolution remote sensing image is difficult to cover all of the study area. It is an object oriented method for fine extraction of impervious surface in urban area of high resolution remote sensing image. The study area is Ludwig, Germany. Combining the digital orthophoto image with nDSM data with the resolution of 0.09m, the ground cover classification of the study area is carried out by using the object-oriented image classification method, in which the spectrum, texture feature and elevation feature of the nDSM are used simultaneously. Support vector machine (SVM), random forest, regular classification and fuzzy membership function are used to classify the classification. The accuracy of the classification is evaluated objectively by using the overall classification accuracy and Kappa coefficient. The experimental results show that, The overall classification accuracy of support vector machine, random forest, fuzzy membership function classification and regular classification are 100.005 and 99.05, respectively. The Kappa coefficient is 1.00000.98710.9871and 0.8840.
【學位授予單位】：山東農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P237

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