發(fā)布時間：2019-03-15 18:29

【摘要】：遙感技術(shù)是對地觀測,獲取地面空間信息的主要技術(shù)手段之一。隨著遙感技術(shù)的不斷發(fā)展,高分辨率遙感影像的研究和應(yīng)用越來越受到重視。高分辨率遙感影像的出現(xiàn),“面向?qū)ο蟆庇跋穹诸惙椒S之崛起,圍繞它的研究和應(yīng)用由點及面地推廣開來,運用于城市制圖、城市土地動態(tài)變化監(jiān)測、公共安全管理、突發(fā)情況預警等方面的研究,可以說面向?qū)ο髨D像分析(objectoriented image analysis,OBIA)已經(jīng)在多個領(lǐng)域碩果累累。與傳統(tǒng)的基于像元的遙感影像分類的方法相比,面向?qū)ο笥跋穹诸惸芸朔敖符}噪聲”和“混合像元”等諸多問題,通過影像分割算法,將圖像分成若干“圖像對象”,借助于這些對象共有的幾何特征、光譜特征、紋理特征和上下文關(guān)系等,能對每一類地物進行準確的提取,具有巨大的優(yōu)勢。但是目前利用面向?qū)ο蠓诸惖难芯恐饕性谏�、農(nóng)業(yè)、城郊等地帶,直接面向城市內(nèi)部復雜地物提取的研究還比較少,且這些研究的研究區(qū)域都較小,未能充分利用這些“圖像對象”的特征來提取復雜的空間信息,沒有體現(xiàn)面向?qū)ο蠓诸惖膬?yōu)勢。針對這些問題,本文從整體出發(fā),以提取城市空間信息為目的,以北京市為研究區(qū)域,以1m空間分辨率的GF-2多光譜與全色融合影像為數(shù)據(jù)支撐,按照面向?qū)ο蠓诸愃枷?以本文提出的V-I-W模型為理論指導,對主要的城市空間信息進行組織,將城市空間信息分為3大類14小類,通過多尺度分割、光譜差異分割來獲取GF-2影像的“圖像對象”,按照光譜特征、波段值特征、幾何特征、紋理特征、和上下文關(guān)系建立各空間信息的提取規(guī)則,從而提取主要的城市空間信息,構(gòu)建RS與GIS的橋梁,用于城市空間分析應(yīng)用。本文的研究結(jié)果表明,城市空間信息提取的總體精度為83.24%,Kappa系數(shù)為0.8069。單一類別的地物提取方面,植被,河流提取精度較高,高于80%,城市道路,房屋等建筑的提取精度稍低,總體的空間信息提取結(jié)果符合預期。
[Abstract]:Remote sensing is one of the main technical means for Earth observation and acquisition of ground space information. With the development of remote sensing technology, more and more attention has been paid to the research and application of high-resolution remote sensing images. With the emergence of high-resolution remote sensing images, the object-oriented image classification method has emerged. The research and application of object-oriented image classification has been extended from point to area and applied to urban cartography, urban land dynamic change monitoring, public safety management, and so on. It can be said that object-oriented image analysis (objectoriented image analysis,OBIA) has already achieved great achievements in many fields, such as emergency early warning and so on. Compared with the traditional pixel-based remote sensing image classification method, object-oriented image classification can overcome many problems such as "pepper-salt noise" and "mixed pixel". Through image segmentation algorithm, the image can be divided into several "image objects". With the help of common geometric features, spectral features, texture features and context relations, each kind of objects can be extracted accurately, which has great advantages. But at present, the research of object-oriented classification is mainly focused on forest, agriculture, suburb and so on. There are still few studies directly facing the extraction of complex features within the city, and the research areas of these studies are small, and the research area of these studies is relatively small. The features of these "image objects" are not fully utilized to extract complex spatial information, which does not reflect the advantages of object-oriented classification. To solve these problems, this paper aims at extracting the urban spatial information, taking Beijing as the research area, taking the 1 m spatial resolution GF-2 multi-spectral and panchromatic fusion image as the data support, according to the object-oriented classification idea, and aiming at extracting the urban spatial information, taking Beijing as the research area. Under the guidance of the V-I-W model proposed in this paper, the main urban spatial information is organized, and the urban spatial information is divided into 3 categories and 14 sub-categories, and the urban spatial information is divided into three categories and 14 sub-categories through multi-scale segmentation. Spectral difference segmentation is used to obtain "image objects" of GF-2 images. According to spectral features, band value features, geometric features, texture features, and context relations, the extraction rules of spatial information are established. Thus, the main urban spatial information is extracted, and the bridge between RS and GIS is constructed, which can be used in urban spatial analysis. The results of this paper show that the overall accuracy of urban spatial information extraction is 83.24%, and the Kappa coefficient is 0.8069. The extraction precision of vegetation and river is higher than 80%. The extraction accuracy of urban roads and buildings is lower than that of other buildings. The overall spatial information extraction results are in line with expectations.
【學位授予單位】：中國地質(zhì)大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P237

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