本文選題：結構特征 + 立體特征　； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：建筑物作為重要的人工地物之一,與人們的生活息息相關。在數(shù)據(jù)挖掘、無人駕駛、精確制導等諸多領域中對于建筑物的識別有著潛在的需求。傳統(tǒng)的利用二維數(shù)據(jù)的建筑物識別技術面對低分辨率影像、結構復雜或形狀相似的建筑物時魯棒性很差,因此利用三維數(shù)據(jù)對建筑物進行識別具有著重要的研究價值和深遠意義。本論文主要研究建筑物的立體識別方法,根據(jù)常見建筑物的頂面類型將其分為脊形、人字形、平頂和錐形四類,利用分辨率為0.1m的DSM數(shù)據(jù),通過提取建筑物的結構特征與立體特征對其進行立體識別。論文中首先對四類頂面建筑物的三維結構進行了分析,針對目標建筑物DSM數(shù)據(jù)中含有大量奇異點與噪聲點的現(xiàn)象,利用了一種改進的區(qū)域增長算法對頂面進行分割,并通過邊緣提取技術得到初始拓撲結構。在此基礎之上,利用形態(tài)學方法對初始拓撲結構進行了孔洞填充,然后利用道格拉斯-普克法對畸形的輪廓線結構進行了幾何校正,最后對拓撲結構分別進行旋轉、尺度校正,生成了建筑物的結構特征。針對目標識別中的關鍵問題,即特征的旋轉、尺度與平移不變性,論文研究了一種點對直方圖特征。首先應用K維樹、K鄰近檢索以及主成分分析方法對目標表面點的法向量進行了估計。根據(jù)大型建筑物表面點數(shù)量大、高噪聲的特性,對適用于小目標識別的立體特征進行了改進,在含有不同高程、尺度以及多角度的目標樣本條件下進行了立體識別,并對比了在不同特征參數(shù)下、不同噪聲環(huán)境中的識別結果,驗證了該特征對不同類別建筑物的分辨力以及魯棒性。對于既定目標的個體識別,論文研究了一種3-D形狀上下文特征,并利用球諧變換對特征進行了改進,解決了旋轉不變性的問題。最后分別在同樣樣本條件下和不同特征參數(shù)條件下進行了特征匹配,獲取最優(yōu)參數(shù)與識別精度,也驗證了特征的旋轉不變性。論文最后對校正的拓撲結構進行了角點與輪廓線的合并,并生成特征向量。利用主成分分析法對點對直方圖特征進行了降維。通過協(xié)同結構特征與點對直方圖特征,在不同噪聲環(huán)境下對建筑物的類型進行了立體識別。識別結果驗證了協(xié)同特征的抗噪能力,相比于僅用立體特征識別具有更高的精度以及魯棒性。
[Abstract]:As one of the most important human site objects, buildings are closely related to people's lives. In many fields, such as data mining, unmanned driving, precision guidance and so on, there is a potential demand for building identification. The traditional building recognition technology based on two-dimensional data has poor robustness in the face of low-resolution images and complex structures or similar shapes. Therefore, the use of three-dimensional data for building identification has an important research value and far-reaching significance. In this paper, the method of stereoscopic recognition of buildings is studied, which is divided into four types: ridged, herringbone, flat-top and conical according to the types of top surfaces of common buildings. The DSM data with a resolution of 0.1 m are used in this paper. The structure features and stereoscopic features of buildings are extracted for stereoscopic recognition. In this paper, the three dimensional structure of four kinds of roof buildings is analyzed. Aiming at the phenomenon that there are a lot of singular points and noise points in the DSM data of the target building, an improved region growth algorithm is used to segment the top surface. The initial topology is obtained by edge detection. On this basis, the initial topological structure is filled with holes by morphological method, then the contour structure of the deformity is corrected by using the Dogas-Puck method, and the topological structure is rotated and calibrated respectively. The structural features of the building are generated. Aiming at the key problems in target recognition, namely, the rotation of feature, the invariance of scale and translation, a point pair histogram feature is studied in this paper. Firstly, the normal vectors of target surface points are estimated by K-dimensional tree K-neighborhood retrieval and principal component analysis (PCA). According to the characteristics of large number of surface points and high noise of large buildings, the stereo features suitable for small target recognition are improved, and the stereo recognition is carried out under the condition of different elevation, scale and multi-angle target samples. The recognition results in different noise environments under different characteristic parameters are compared to verify the resolution and robustness of the feature to different types of buildings. For individual recognition of a given target, a 3-D shape context feature is studied, and the spherical harmonic transformation is used to improve the feature, which solves the problem of rotation invariance. Finally, the feature matching is carried out under the same sample condition and different characteristic parameter condition, the optimal parameter and recognition accuracy are obtained, and the rotation invariance of the feature is verified. Finally, the corrected topology is combined with the contour and the eigenvector is generated. The principal component analysis (PCA) is used to reduce the dimension of dot pair histogram. Based on the features of cooperative structure and point-pair histogram, the types of buildings are identified in different noise environments. The recognition results show that the anti-noise ability of cooperative features is more accurate and robust than that of only stereoscopic features.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P208

【參考文獻】

相關期刊論文 前10條

1 張愛武;李文寧;段乙好;孟憲剛;王書民;李含倫;;結合點特征直方圖的點云分類方法[J];計算機輔助設計與圖形學學報;2016年05期

2 姜文;劉立康;;基于聯(lián)合兩種特征的手寫體維文字符識別[J];計算機工程與應用;2017年05期

3 李艷麗;尹念東;;基于多特征聯(lián)合的多目標識別算法研究[J];湖北理工學院學報;2014年05期

4 于海洋;余鵬磊;謝秋平;李寧;盧小平;;機載LiDAR數(shù)據(jù)建筑物頂面點云分割方法研究[J];測繪通報;2014年06期

5 李云帆;馬洪超;;從LiDAR數(shù)據(jù)中提取建筑物平面目標的新方法[J];計算機工程與應用;2011年10期

6 李寶;程志全;黨崗;金士堯;;三維點云法向量估計綜述[J];計算機工程與應用;2010年23期

7 馮鏡蒯;;基于SIFT特征匹配算法的城市地點識別系統(tǒng)[J];電腦與電信;2009年08期

8 承德保;;基于多特征組的遙感圖像中建筑物目標自動識別與標繪的方法[J];電子與信息學報;2008年12期

9 趙潔;李光耀;龐池海;朱恒曄;;基于小波變換的衛(wèi)星遙感地圖中建筑物識別[J];計算機技術與發(fā)展;2008年11期

10 田野;趙春暉;季亞新;;主成分分析在高光譜遙感圖像降維中的應用[J];哈爾濱師范大學自然科學學報;2007年05期

相關博士學位論文 前1條

1 閆奕名;多角度目標立體信息優(yōu)化獲取及識別技術[D];哈爾濱工業(yè)大學;2013年

相關碩士學位論文 前5條

1 朱忠國;基于高分辨率遙感影像的城市建筑物目標識別[D];北京建筑大學;2015年

2 劉正;三維點云法向量估計方法研究[D];華北電力大學;2015年

3 楊小娜;基于形狀上下文的目標形狀識別與匹配[D];昆明理工大學;2013年

4 林源;基于航空立體多影像的城市建筑物識別技術研究[D];北京理工大學;2011年

5 孫寧;面向高空間分辨率遙感影像的建筑物目標識別方法研究[D];浙江大學;2010年

，

本文編號：1922995