CAJ | 학술논문

当前机载激光雷达数据和影像匹配得到的点云是密集点云数据的两类主要来源，但都不可避免存在着噪声点。本文提出一种新的点云去噪算法，可适用于这两类数据中所包含的噪声点的去除。算法主要包括两步：第1步利用多尺度的密度算法去除孤立噪声和小的簇状噪声；第2步利用三角网约束将第1步中误检测为噪声的点重新归为正常点。针对真实数据进行了剔噪试验，结果表明本文提出的基于密度分析的多尺度噪声检测算法对孤立噪声和簇状噪声都有较为效，且对于质量较差的影像匹配点云的检测也能有效处理。本文算法检测率达到97％以上。
당전궤재격광뢰체수거화영상필배득도적점운시밀집점운수거적량류주요래원，단도불가피면존재착조성점。본문제출일충신적점운거조산법，가괄용우저량류수거중소포함적조성점적거제。산법주요포괄량보：제1보이용다척도적밀도산법거제고립조성화소적족상조성；제2보이용삼각망약속장제1보중오검측위조성적점중신귀위정상점。침대진실수거진행료척조시험，결과표명본문제출적기우밀도분석적다척도조성검측산법대고립조성화족상조성도유교위효，차대우질량교차적영상필배점운적검측야능유효처리。본문산법검측솔체도97％이상。
Laser scanning and image matching are both effective ways to get dense point cloud data , however ,outliers obtained from both ways are still inevitable .A novel hierarchical outlier detection method is proposed for the automatic outlier detection of point cloud from image matching and airborne laser scanning .There are two main steps in this method .Firstly ,the hierarchical density estimation is used to remove single and small cluster outliers .Then a progressive TIN method is used to find non‐outliers removed in the previous steps .The experimental results indicate the effectiveness of this method in dealing with the two types of points cloud data .And this method can also handle low quality point cloud data from image matching .The quantitative analysis shows that the outlier detection rate is higher than 97% .