本文選題：SOM神經(jīng)網(wǎng)絡(luò) + 道路網(wǎng)��； 參考：《南京大學(xué)》2013年碩士論文

【摘要】：在地圖制圖中,因?yàn)榈貓D圖幅有限,地圖上不可能將研究區(qū)內(nèi)所有的要素表示出來,因此只能根據(jù)地圖用途、地圖比例尺和制圖區(qū)域特點(diǎn),將制圖對(duì)象中的規(guī)律性和典型特征以概括和抽象的形式表示出來,而對(duì)于那些次要的和非本質(zhì)的地物要素則要舍棄。當(dāng)?shù)貓D由大比例尺縮小到小比例尺時(shí),地圖的面積是按等比級(jí)數(shù)縮小的,如果將地圖上的要素也按照這種方式縮小的話,地物要素將出現(xiàn)扭曲、壓蓋、變形等現(xiàn)象。為了解決地圖要素與實(shí)地地物之間的矛盾,則需要用到制圖綜合,它通過對(duì)地圖內(nèi)容的選取、化簡、概括和位移,可以建立反映區(qū)域地理規(guī)律和特點(diǎn)的新的地圖模型。傳統(tǒng)的制圖綜合主要是靠手工實(shí)現(xiàn),這種制圖方式要求繁瑣的手工勞動(dòng),而且存在很大的主觀性。此外,手工制圖綜合的質(zhì)量也會(huì)受到很多人為因素的影響,所以很難保證地圖的質(zhì)量和品質(zhì)�，F(xiàn)代制圖綜合指的是計(jì)算機(jī)環(huán)境下的制圖綜合,數(shù)字制圖技術(shù)在很大程度上促進(jìn)了地圖生產(chǎn)效率的提高,為地圖自動(dòng)制圖綜合的研究提供了技術(shù)基礎(chǔ)。在數(shù)字制圖技術(shù)條件下,一方面地圖自動(dòng)制圖綜合將制圖人員從繁雜的手工操作中解脫出來,另一方面制圖人員可以投入更多的精力研究如何提高制圖綜合的自動(dòng)化程度。在計(jì)算機(jī)技術(shù)和圖形交互編輯功能不斷提高的背景下,自動(dòng)制圖綜合的理論和應(yīng)用也取得了很大的進(jìn)步,但仍然存在很多難以克服的問題,比較突出的問題是制圖綜合的自動(dòng)綜合程度還不是很高。人工智能技術(shù)在自動(dòng)制圖綜合領(lǐng)域中的應(yīng)用是該領(lǐng)域的一個(gè)重要突破,因?yàn)槿斯ぶ悄芗夹g(shù)具有一定的人腦思維能力,從而可以在某種程度上模擬人類制圖綜合的過程。人工神經(jīng)網(wǎng)絡(luò)是一種采用物理可實(shí)現(xiàn)系統(tǒng)來模擬人腦神經(jīng)細(xì)胞的結(jié)構(gòu)和功能的系統(tǒng),它具有自學(xué)習(xí)、聯(lián)想存儲(chǔ)、和高速尋找優(yōu)化解的功能,因此可以把它應(yīng)用于制圖綜合中。線狀要素的制圖綜合是制圖綜合領(lǐng)域中的研究熱點(diǎn)和重點(diǎn)。道路網(wǎng)遍布全圖,形狀多樣、關(guān)系復(fù)雜、等級(jí)繁多,是所有地圖要素中比較重要、使用頻率較高的數(shù)據(jù)層,有著重要的經(jīng)濟(jì)和軍事意義。因此,使用人工神經(jīng)網(wǎng)絡(luò)研究道路網(wǎng)的制圖綜合具有重要的意義。本研究使用一種方法,它將道路的拓?fù)�、幾何和語義屬性輸入到一個(gè)自組織競爭神經(jīng)網(wǎng)絡(luò)中,自組織競爭神經(jīng)網(wǎng)絡(luò)是一種人工神經(jīng)網(wǎng)絡(luò),在此研究中用于對(duì)道路網(wǎng)的聚類分析。更具體地說,該方法根據(jù)多種屬性將所有的道路分成不同的類,然后基于這些分類在比例尺縮小的地圖上按照某種指標(biāo)對(duì)道路進(jìn)行選取。傳統(tǒng)的道路選取方法主要是根據(jù)道路等級(jí)等語義屬性進(jìn)行選取的,而忽視了道路的空間特性,本文分別使用了道路的拓?fù)�、幾何和語義屬性將道路進(jìn)行聚類,考慮比較全面,因此聚類結(jié)果更加準(zhǔn)確,在此基礎(chǔ)上對(duì)道路進(jìn)行選取也可以得到更好的效果。
[Abstract]:In cartography, it is not possible to express all the elements of the study area on a map because of its limited map size, and therefore can only be based on the purpose of the map, the map scale and the characteristics of the cartographic area, The regularity and typical features of the mapping object are expressed in the form of generalization and abstraction, but the secondary and non-essential elements of the feature should be abandoned. When the map is reduced from a large scale to a small scale, the area of the map is reduced according to the equal-ratio series. If the elements on the map are also reduced in this way, the elements of the feature will be distorted, overlaid, deformed and so on. In order to solve the contradiction between map elements and field features, cartographic generalization is needed. Through the selection, simplification, generalization and displacement of map contents, a new map model can be established to reflect the laws and characteristics of regional geography. Traditional cartographic generalization is mainly realized by hand, which requires complicated manual work and has great subjectivity. In addition, the quality of manual cartography generalization is also affected by many man-made factors, so it is difficult to ensure the quality and quality of maps. Modern cartographic generalization refers to cartographic generalization in computer environment. Digital cartography technology promotes the efficiency of map production to a great extent and provides a technical basis for the research of automatic cartographic generalization. Under the condition of digital cartography technology, on the one hand, automatic cartographic generalization can free cartographers from complicated manual operation, on the other hand, cartographers can devote more energy to study how to improve the automation of cartographic generalization. With the continuous improvement of computer technology and interactive editing of graphics, great progress has been made in the theory and application of automatic cartographic generalization, but there are still many insurmountable problems. The outstanding problem is that the degree of automatic generalization of cartographic generalization is not very high. The application of artificial intelligence technology in the field of automatic cartographic generalization is an important breakthrough in this field, because artificial intelligence technology has certain human brain thinking ability, so it can simulate the process of human cartographic generalization to some extent. Artificial neural network (Ann) is a physical system which can simulate the structure and function of human neural cells. It has the functions of self-learning, associative storage and high-speed searching for optimal solution, so it can be applied to cartographic generalization. Cartographic generalization of linear elements is the focus of research in the field of cartographic generalization. Road network is the most important data layer in all the map elements and has important economic and military significance. Therefore, it is of great significance to use artificial neural network to study road network cartographic generalization. In this study, a method is used to input the topology, geometry and semantic attributes of the road into a self-organizing competitive neural network, which is an artificial neural network, which is used in the clustering analysis of the road network. More specifically, the method divides all the roads into different classes according to various attributes, and then selects the roads according to a certain index on a scaled down map based on these classifications. The traditional road selection method is mainly based on the semantic attributes such as road grade, but neglects the spatial characteristics of the road. In this paper, the road topology, geometry and semantic attributes are used to cluster the road. Therefore, the clustering results are more accurate, and better results can be obtained on the basis of the selection of roads.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P283.7

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 錢海忠;武芳;王家耀;;自動(dòng)制圖綜合鏈理論與技術(shù)模型[J];測繪學(xué)報(bào);2006年04期

2 王橋，，吳紀(jì)桃;一種新分維估值方法作為工具的自動(dòng)制圖綜合[J];測繪學(xué)報(bào);1996年01期

3 武芳,侯璇,錢海忠,鄧紅艷;自動(dòng)制圖綜合中的線目標(biāo)位移模型[J];測繪學(xué)報(bào);2005年03期

4 劉志勇,許捍衛(wèi),柯紅軍;數(shù)字環(huán)境下的自動(dòng)制圖綜合探討[J];現(xiàn)代測繪;2005年S1期

5 王振宇,劉艷芳,焦利民,謝毅;農(nóng)用地分等省級(jí)匯總中等別圖的自動(dòng)制圖綜合[J];測繪信息與工程;2005年04期

6 田震,王家耀,武芳;自動(dòng)制圖綜合人工神經(jīng)元網(wǎng)絡(luò)方法的研究[J];解放軍測繪學(xué)院學(xué)報(bào);1998年04期

7 安敏;自動(dòng)制圖綜合中地貌數(shù)據(jù)庫的拼接[J];測繪通報(bào);1993年01期

8 桑琴;付曉文;;最小二乘法在自動(dòng)制圖綜合中的應(yīng)用[J];西部探礦工程;2006年09期

9 張洪升;王金娜;;矢量曲線自動(dòng)綜合算法的研究[J];測繪與空間地理信息;2011年03期

10 黃麗娜;何津;費(fèi)立凡;;基于AO的水系自動(dòng)制圖綜合應(yīng)用研究[J];地理空間信息;2007年04期

相關(guān)會(huì)議論文 前4條

1 錢海忠;武芳;孫建東;葛磊;朱強(qiáng);王家耀;;自動(dòng)制圖綜合鏈理論與技術(shù)模型[A];中國測繪學(xué)會(huì)九屆四次理事會(huì)暨2008年學(xué)術(shù)年會(huì)論文集[C];2008年

2 劉志勇;許捍衛(wèi);柯紅軍;;數(shù)字環(huán)境下的自動(dòng)制圖綜合探討[A];'2005數(shù)字江蘇論壇——電子政務(wù)與地理信息技術(shù)論文專輯[C];2005年

3 劉志勇;許捍衛(wèi);柯紅軍;;數(shù)字環(huán)境下的自動(dòng)制圖綜合探討[A];'2005數(shù)字江蘇論壇電子政務(wù)與地理信息技術(shù)論文專輯[C];2005年

4 彭哠;劉兆禮;;遙感影像復(fù)雜顯著地物的自動(dòng)制圖綜合研究[A];2001年東北三省測繪學(xué)術(shù)與信息交流會(huì)論文集[C];2001年

相關(guān)碩士學(xué)位論文 前3條

1 楊萌萌;SOM神經(jīng)網(wǎng)絡(luò)在道路網(wǎng)制圖綜合中的應(yīng)用[D];南京大學(xué);2013年

2 宋振;自動(dòng)制圖綜合中空間沖突檢測研究[D];解放軍信息工程大學(xué);2010年

3 胡濱;麗江市“兩權(quán)”調(diào)查中地圖自動(dòng)綜合理論研究與應(yīng)用[D];昆明理工大學(xué);2014年

本文編號(hào)：1840193