【摘要】：在遙感技術(shù)、智能計(jì)算和云計(jì)算計(jì)算高度發(fā)展的今天,雖然高分辨率遙感影像處理技術(shù)成果層出不窮,但高分辨率影像道路自動(dòng)提取仍是當(dāng)今研究熱點(diǎn)之一。在諸如地圖導(dǎo)航應(yīng)用、土地資源監(jiān)測、城鄉(xiāng)規(guī)劃等領(lǐng)域,高分辨率影像道路自動(dòng)提取已經(jīng)成為不可或缺又亟待提升關(guān)鍵技術(shù)。如何在高分辨率影像復(fù)雜的紋理和幾何信息中準(zhǔn)確的獲取道路信息仍然是高分影像目標(biāo)提取的重點(diǎn)課題。傳統(tǒng)的影像道路提取,尚未有效地顧及高分辨率影像中豐富的紋理和幾何特征,提取結(jié)果存在不同程度的問題。為此,本文提出了基于高分辨率影像道路多種特征進(jìn)行綜合分析的方法,進(jìn)行高分辨率影像道路提取,其中采用正向定位和反向剔除的提取思想,利用最小外接矩形和最小外接圓,從多方面考慮道路提取條件,較好解決了高分辨率影像道路提取問題。本文主要研究高分辨率航空影像各類特征進(jìn)行提取方法,獲取道路的紋理特征和幾何特征,采用正向定位和反向剔除的整體思路對(duì)道路進(jìn)行定位。提出使用紋理和Gabor提取的方式確定道路的位置,采用最小外接矩形和最小外接圓對(duì)道路之外的地物進(jìn)行搜索并剔除,從道路本身和道路之外的地物兩個(gè)方面著手,配合影像分割方法、運(yùn)用多特征分析、最小外接矩形和外接圓提取和數(shù)學(xué)形態(tài)學(xué)的應(yīng)用準(zhǔn)確的對(duì)道路進(jìn)行提取。本文多特征綜合分析提取的流程為:(1)影像道路紋理特征提取(2)影像道路區(qū)域分割(3)影像道路分類(4)多特征分析(5)數(shù)學(xué)形態(tài)學(xué)精細(xì)處理。最后利用C++設(shè)計(jì)開發(fā)了高分辨率影像道路提取實(shí)驗(yàn)平臺(tái),分別選取城市道路影像和山區(qū)道路影像為實(shí)驗(yàn)影像。實(shí)驗(yàn)表明,本文提出多特征綜合分析處理方法能較好的提取高分辨率遙感影像中道路結(jié)構(gòu),可以有針對(duì)性的設(shè)定閾值對(duì)影像進(jìn)行處理。相比較前人提取算法,在提取效果方面有較大提升。在實(shí)驗(yàn)過程中發(fā)現(xiàn),城市道路提取和山區(qū)道路提取有一定的閾值設(shè)定規(guī)律,并有針對(duì)性的加以總結(jié)。雖然本文提出的流程和提取分析方法能夠較好的提取道路,但是在閾值設(shè)定的自適應(yīng)性方面還需要做進(jìn)一步的研究,同時(shí)針對(duì)陰影遮擋較為嚴(yán)重的路面的提取也需要進(jìn)一步深入研究。
[Abstract]:With the development of remote sensing technology, intelligent computing and cloud computing, although high-resolution remote sensing image processing technology results in endlessly, but high-resolution image road automatic extraction is still one of the hot spots. In such fields as map navigation, land resource monitoring, urban and rural planning, high-resolution image automatic road extraction has become an indispensable and urgent need to improve the key technology. How to obtain road information accurately in the complex texture and geometry information of high resolution image is still the key task of target extraction in high resolution image. Traditional road extraction has not yet taken into account the rich texture and geometric features of high-resolution images, and there are some problems in the extraction results. Therefore, this paper puts forward a comprehensive analysis method based on many features of high-resolution image road, and carries out road extraction of high-resolution image, in which the idea of forward location and reverse culling is adopted. By using the minimum outer rectangle and the minimum circumscribed circle, the road extraction condition is considered from many aspects, and the problem of road extraction in high resolution image is better solved. In this paper, we mainly study the methods of extracting all kinds of features from high-resolution aerial images, obtain the texture features and geometric features of the road, and use the whole idea of forward location and reverse culling to locate the road. The method of texture and Gabor extraction is used to determine the location of the road, and the minimum outer rectangle and the minimum circumscribed circle are used to search and eliminate the objects outside the road, which start from two aspects: the road itself and the objects outside the road. In combination with the image segmentation method, using multi-feature analysis, the minimum external rectangle and circumscribed circle extraction and mathematical morphology of the application of accurate road extraction. The process of multi-feature analysis and extraction is as follows: (1) road texture feature extraction (2) image road segmentation (3) image road classification (4) multi-feature analysis (5) mathematical morphology fine processing. Finally, an experimental platform for high resolution image road extraction is developed by using C, and urban road images and mountain road images are selected as experimental images. The experimental results show that the multi-feature comprehensive analysis and processing method proposed in this paper can extract the road structure in the high-resolution remote sensing image and can set the threshold to process the image. Compared with the previous algorithms, the extraction effect has been greatly improved. In the course of experiment, it is found that there are certain threshold setting rules in urban road extraction and mountain road extraction, and summarized accordingly. Although the process and extraction analysis method proposed in this paper can extract the road well, but in the adaptive aspect of threshold setting, we still need to do further research. At the same time, the extraction of more serious shading pavement also needs further research.
【學(xué)位授予單位】：北京建筑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P237

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李正兵;羅斌;翟素蘭;涂錚錚;;基于關(guān)聯(lián)圖劃分的Kmeans算法[J];計(jì)算機(jī)工程與應(yīng)用;2013年21期

2 王爽;夏玉;焦李成;;基于均值漂移的自適應(yīng)紋理圖像分割方法[J];軟件學(xué)報(bào);2010年06期

3 李光耀;胡陽;;高分辨率遙感影像道路提取技術(shù)研究與展望[J];遙感信息;2008年01期

4 文志強(qiáng);蔡自興;;Mean Shift算法的收斂性分析[J];軟件學(xué)報(bào);2007年02期

5 薄華;馬縛龍;焦李成;;圖像紋理的灰度共生矩陣計(jì)算問題的分析[J];電子學(xué)報(bào);2006年01期

6 李鄉(xiāng)儒,吳福朝,胡占義;均值漂移算法的收斂性[J];軟件學(xué)報(bào);2005年03期

7 王植,賀賽先;一種基于Canny理論的自適應(yīng)邊緣檢測方法[J];中國圖象圖形學(xué)報(bào);2004年08期

8 陳嵐嵐,畢篤彥;數(shù)學(xué)形態(tài)學(xué)在圖像處理中的應(yīng)用[J];現(xiàn)代電子技術(shù);2002年08期

9 戴青云,余英林;數(shù)學(xué)形態(tài)學(xué)在圖象處理中的應(yīng)用進(jìn)展[J];控制理論與應(yīng)用;2001年04期

10 蔡濤,王潤生;一個(gè)從多波段遙感圖像提取道路網(wǎng)的算法[J];軟件學(xué)報(bào);2001年06期

相關(guān)碩士學(xué)位論文 前3條

1 張艷;基于Gabor濾波器的紋理特征提取研究及應(yīng)用[D];西安科技大學(xué);2014年

2 李娜;基于數(shù)學(xué)形態(tài)學(xué)的藻類圖像去噪算法研究[D];中國海洋大學(xué);2013年

3 周秋琳;顧及幾何特征的數(shù)學(xué)形態(tài)學(xué)高分辨率遙感道路提取方法研究[D];中南大學(xué);2013年

，

本文編號(hào)：2395067