本文關(guān)鍵詞： 高分辨率遙感影像 道路幾何特征 數(shù)學(xué)形態(tài)學(xué) 道路提取　出處：《中南大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著遙感技術(shù)和計(jì)算機(jī)技術(shù)的快速發(fā)展,高分辨率遙感為人們提供了高質(zhì)量和豐富的數(shù)據(jù)源。道路是基礎(chǔ)地理信息中的重要基礎(chǔ)要素,道路網(wǎng)的高效率、高精度提取對地圖制圖、數(shù)據(jù)更新具有十分重要的意義,而高分辨率遙感技術(shù)已成為道路網(wǎng)信息獲取的重要手段。道路信息在遙感影像中呈現(xiàn)出以下特征：寬度基本一致長度長的幾何特征、灰度在局部區(qū)域變化緩慢的輻射特征、魯棒性強(qiáng)的光譜特征、道路之間相互連接成網(wǎng)絡(luò)的拓?fù)涮卣�、道路在現(xiàn)實(shí)中所實(shí)現(xiàn)的功能特征以及道路與周圍區(qū)域相關(guān)聯(lián)的上下文特征。如何從豐富的高分辨率遙感數(shù)據(jù)中提取道路網(wǎng)是遙感目標(biāo)提取的重要內(nèi)容。 數(shù)學(xué)形態(tài)學(xué)作為應(yīng)用數(shù)學(xué)的一個(gè)重要分支,它是通過探測目標(biāo)圖像的形態(tài)特征來研究目標(biāo)圖像的空間結(jié)構(gòu)從而對圖像進(jìn)行各種處理的分析工具,它的基本思想是用具有一定形態(tài)特征的結(jié)構(gòu)元素去度量和提取圖像中對應(yīng)的形狀目標(biāo)以達(dá)到對圖像分析和識別的目的。數(shù)學(xué)形態(tài)學(xué)在計(jì)算機(jī)視覺、圖像處理和分析、模式識別、測繪等領(lǐng)域得到了廣泛的應(yīng)用。本文基于數(shù)學(xué)形態(tài)學(xué)的基礎(chǔ)理論和算法,結(jié)合遙感影像中道路的幾何特征對高分辨率遙感影像的道路網(wǎng)信息進(jìn)行提取。本文的主要內(nèi)容有： 1.簡要敘述了道路網(wǎng)提取的意義,介紹了國內(nèi)外數(shù)學(xué)形態(tài)學(xué)應(yīng)用于圖像處理和道路信息獲取的研究概況。 2.介紹了數(shù)學(xué)形態(tài)學(xué)的理論和基本運(yùn)算,主要對二值形態(tài)學(xué)和灰度形態(tài)學(xué)的理論、算法和代數(shù)性質(zhì)做了闡述。 3.分析了高分辨率遙感影像道路提取的特殊性與難點(diǎn),運(yùn)用數(shù)學(xué)形態(tài)學(xué)開、閉運(yùn)算,Top-Hat變換,閾值分割,形態(tài)細(xì)化等算法,在模擬影像上實(shí)現(xiàn)了道路信息的精確提取。 4.在對遙感影像中道路特征進(jìn)行深入研究與分析的基礎(chǔ)上,充分顧及道路的幾何特征,把在模擬影像上準(zhǔn)確實(shí)現(xiàn)道路提取的數(shù)學(xué)形態(tài)學(xué)算法應(yīng)用于實(shí)驗(yàn)影像中的道路網(wǎng)提取。采用形態(tài)開運(yùn)算對實(shí)驗(yàn)影像去噪；閉運(yùn)算用于填補(bǔ)道路提取過程中的漏洞；利用閾值分割方法對影像進(jìn)行分割；最后通過細(xì)化運(yùn)算得到道路的中心線信息,進(jìn)而得到完整的道路網(wǎng)提取結(jié)果。 本項(xiàng)研究表明,在充分顧及遙感影像中道路幾何特征的基礎(chǔ)上,運(yùn)用數(shù)學(xué)形態(tài)學(xué)方法在高分辨率遙感影像中提取道路,具有較好的提取效果和較高的幾何精度。
[Abstract]:With the rapid development of remote sensing technology and computer technology, high-resolution remote sensing provides people with high quality and rich data sources. High precision extraction is very important to map mapping and data updating. High-resolution remote sensing technology has become an important means of road network information acquisition. Road information in remote sensing images show the following characteristics: the width of the geometric features of the basic length of the same length. The gray level changes slowly in the local region radiation characteristic, the robust spectrum characteristic, the road interconnects the network topology characteristic. How to extract road network from rich high-resolution remote sensing data is an important content of remote sensing target extraction. As an important branch of applied mathematics, mathematical morphology is an analytical tool to study the spatial structure of the target image by detecting the morphological characteristics of the target image. Its basic idea is to measure and extract the corresponding shape objects in the image by using the structural elements with certain morphological characteristics to achieve the purpose of image analysis and recognition. Mathematical morphology in computer vision. Image processing and analysis, pattern recognition, mapping and other fields have been widely used. This paper based on the basic theory and algorithm of mathematical morphology. The road network information of high resolution remote sensing image is extracted by combining the geometric characteristics of road in remote sensing image. The main contents of this paper are as follows: 1. The significance of road network extraction is briefly described, and the research situation of the application of mathematical morphology in image processing and road information acquisition at home and abroad is introduced. 2. The theory and basic operation of mathematical morphology are introduced. The theories, algorithms and algebraic properties of binary morphology and grayscale morphology are discussed. 3. The particularity and difficulty of road extraction in high resolution remote sensing image are analyzed, and the algorithms of mathematical morphology opening, closed operation and Top-Hat transform, threshold segmentation and morphological thinning are used. The accurate extraction of road information is realized on the simulated image. 4. Based on the in-depth study and analysis of road features in remote sensing images, the geometric features of roads are fully taken into account. The mathematical morphology algorithm is applied to the road network extraction in the experimental image, and the morphological open operation is used to de-noise the experimental image. Closed operation is used to fill the loophole in the road extraction process; The threshold segmentation method is used to segment the image. Finally, the central line information of the road is obtained by thinning operation, and the complete road network extraction result is obtained. This study shows that, on the basis of taking full account of the geometric characteristics of the road in remote sensing images, the method of mathematical morphology is used to extract roads in high-resolution remote sensing images. It has better extraction effect and higher geometric precision.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P237

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