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利用數學形態學方法於高程空間區隔地貌與地物之研究

Object Segmentation in Elevation Space Using Mathematic Morphology

摘要


在數位航測工作站中以自動影像匹配,或利用空載雷射掃描儀獲取地表面的數值高程,原為混雜有地貌以及人工物、樹木等地物的高程資料,本文嘗試同時引用兩種數學形態學方法來處理此種原始數值表面模型,在高程空間中分離地貌與地物的點位。文中主要的處理程序有二,第一是利用開放操作來濾除高程空間的雜訊,第二再利用H-Dome轉換區隔高程空間的局部較高區,也就是本文所定義的地物區。文中以中央大學及瑞士聯邦工業大學附近的區域為實驗區,為評估地物區隔能力是否良好,先取房屋部份進行比對,在網格密度為lm的DSM測試實驗中,90%以上的房屋落在高度3m以上的地物區塊內。為進一步完整評估,則檢核能否獲取地貌的DTM。但基於lm網格高程資料的雜訊過多且點位高程的連續情形嚴重,不易完整區隔出地物,故另外取網格密度10m的DSM來產生DTM 。二實驗區的操作結果與人工量測成果相比較,得高程均值誤差分別為0.21m及0.22m、均方根誤差分別為l.04m及l.45m 。

並列摘要


The original Digital Surface Model (DSM) produced from image matching in Digital Photogrammetric Workstation (DPW) or from scanning by Airborne Laser Scanner (ALS) are the mixing representation for ground, buildings, trees and many others. We propose two mathematic morphology methods, Opening Operation and H-Dome Transformation, to reconstruct DSM in elevation space to separate the ground surface from those objects above ground. The procedure of the proposed methods includes: (1)Opening Operation to filter out the noise in elevation space. (2)H-Dome Transformation to detect the local regional maxima where the objects above ground locate. NCU area and ETH area are used for test data in this experiment. There are over 90% of buildings locate on the segmented areas. And for strict assessment by generating DTM from DSM, the results of two test data compared with manual measurement show that the mean errors are 0.21m and 0.22m, and the RIVISE are l.04m and l.45m, respectively.

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