本文選題：高分辨率　切入點：遙感影像　出處：《山東農(nóng)業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：工業(yè)革命使城市化得以在世界范圍內(nèi)快速的發(fā)展�？墒窃谝恍┓蔷C合功能分區(qū)的城市布局理念指導下,城市的最基本功能被人為強制性的割裂開來,相互間缺乏有機聯(lián)系,使內(nèi)部功能失去活性,給居民生活帶來不便。街區(qū)作為城市的基本組成單元,是城市經(jīng)濟、文化、政治等活動的基礎,其功能規(guī)劃優(yōu)劣直接影響著城市的建設與發(fā)展。因此,可以通過研究城市街區(qū)模式來研究城市模式,發(fā)現(xiàn)城市內(nèi)部問題。可是由于城市街區(qū)信息量大、范圍廣等特點,導致獲取城市街區(qū)信息需要耗費大量人力、物力,且周期長、效率較差。隨著電子技術、光學成像技術、網(wǎng)絡傳輸技術等的飛速發(fā)展,遙感對地觀測技術具備了多角度、高空間分辨率、高時間分辨率、高光譜分辨率等優(yōu)勢,可以獲取城市地物詳細信息,且信息覆蓋周期短�；谝陨戏治�,本文提出高分辨率遙感影像城市典型目標提取及街區(qū)功能分類方法,其研究結果包含以下幾個方面:(1)提出了一種面向對象的高分辨率遙感影像陰影提取方法。首先,利用mean shift算法進行地物特征聚類,去除噪聲。然后,使用本文提出的陰影檢測指數(shù)進行陰影檢測。最后,利用閾值分割提取陰影區(qū)域。選取兩景不同場景的實驗數(shù)據(jù)進行了驗證實驗。實驗結果表明,本文方法能準確、有效地提取陰影區(qū)域,且能去除水體、藍色地物等非陰影地物的影響;另外,使用面向對象的思想可以有效地去除噪聲的影響,提高檢測的精確度。(2)提出一種基于感知編組的高分辨率遙感影像主干道路自動提取方法。首先,利用直線段檢測器算法提取影像中直線段信息。然后,利用道路在高分辨率遙感影像上的幾何特征進行感知編組。最后,經(jīng)過長度約束得到道路信息。使用兩景不同場景、不同傳感器的實驗數(shù)據(jù)進行驗證實驗。實驗結果表明,兩個實驗中道路提取的完整率、正確率和檢測質量都在96%以上。(3)提出一種高分辨率遙感影像城市街區(qū)功能分類方法。首先,通過計算獲取建筑物、植被、水體、陰影的特征影像。然后,結合道路網(wǎng)信息將城市劃分為獨立的街區(qū)影像單元集合,并通過計算每個街區(qū)各特征影像均值的方式,將面向像元的處理方式轉變?yōu)槊嫦蚪謪^(qū)對象的處理方式。最后,通過LIBSVM分類器實現(xiàn)城市街區(qū)功能的分類。實驗結果表明,本文提出的高分辨率遙感影像城市街區(qū)功能分類方法能將城中村、現(xiàn)代居民區(qū)、商業(yè)區(qū)等8類不同功能的街區(qū)很好的分類,且精度都在84%以上。
[Abstract]:The industrial revolution allowed the rapid development of urbanization around the world. However, under the guidance of the concept of urban layout of some non-comprehensive functional zones, the most basic functions of cities were cut apart by artificial compulsion, and there was a lack of organic connection between them. The block, as the basic component unit of the city, is the basis of the city's economic, cultural and political activities, and its function planning directly affects the construction and development of the city. We can study the urban model by studying the urban block model and find out the problems within the city. However, because of the characteristics of large amount of information and wide range of urban blocks, it takes a lot of manpower, material resources and a long period to obtain the information of urban blocks. With the rapid development of electronic technology, optical imaging technology and network transmission technology, remote sensing Earth observation technology has the advantages of multi-angle, high spatial resolution, high time resolution, high spectral resolution, etc. The detailed information of urban features can be obtained, and the information coverage period is short. Based on the above analysis, this paper puts forward a method of extracting typical urban targets and classifying the function of blocks in high-resolution remote sensing images. The research results include the following aspects: 1) an object oriented shadow extraction method for high resolution remote sensing images is proposed. Firstly, the feature clustering of ground objects is carried out by using mean shift algorithm to remove noise. The shadow detection index proposed in this paper is used for shadow detection. Finally, the shadow region is extracted by threshold segmentation. The experimental data of two different scenes are selected for verification. The experimental results show that the proposed method is accurate. The shadow area can be extracted effectively, and the influence of non-shadow objects such as water body, blue ground object and so on can be removed. In addition, the effect of noise can be effectively removed by using object-oriented thought. An automatic trunk road extraction method based on perceptual marshalling is proposed. Firstly, line segment detector algorithm is used to extract the line segment information in the image. Finally, the road information is obtained by length constraint. The experimental data of two different scenes and different sensors are used to validate the experiment. The experimental results show that, In the two experiments, the integrity rate, correct rate and detection quality of road extraction are above 96%.) A high resolution remote sensing image of urban block function classification method is proposed. Firstly, the buildings, vegetation, water body are obtained by calculation. Then, combining the road network information, the city is divided into a set of independent block image units, and by calculating the average value of each feature image in each block, The pixel oriented processing method is transformed into the block oriented object processing mode. Finally, the LIBSVM classifier is used to realize the classification of the urban block function. The experimental results show that, The high resolution remote sensing image of urban block function classification method proposed in this paper can classify 8 different function blocks, such as village in city, modern residential area, commercial district and so on, and the accuracy is more than 84%.
【學位授予單位】：山東農(nóng)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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本文編號：1623060