空載光達的系統性誤差會造成相鄰航帶點位高程偏差，通常應用航帶平差方法來減低系統性誤差的影響。使用這種方法時，必須在不同航帶找出對應區域或連結點的位置。一般以人工選取的方式決定對應區域或連結點的位置，而人工選取的方式費工又費時。本文提出一套自動化選取對應區域或連結點的方法：首先以張量投票法自動偵測光達平面特徵；其次計算同一航帶內所萃取出的平面之位相關係，並將具有相似位相關係的平面以二階段的類神經網路演算法進行匹配，同時提出以二分樹法來提高匹配的正確率；最後再將匹配後得到的共軛平面之重心坐標視為連結點進行航帶平差。本法的好處是連結點的選取工作可以自動化地執行。航帶平差的實驗結果顯示：本文所提出的自動化匹配對應平面區域的方法對於改善空載光達高程精度是相當可行的。

Systematic errors of airborne Lidar data cause elevation offset of point clouds. Strip adjustment is one of the ways to reduce systematic errors. Using strip adjustment, the locations of conjugate blocks or tie points have to be detected first and they usually be manually selected and decided with laborious and time-consuming efforts. The purpose of this study is to develop a method for automatically selecting conjugate blocks or tie points. In this article, the tensor voting method is adopted for the extraction of planar features from Lidar data and an artificial neural network method is applied to match the planes with similar topologic properties. The Bintree method is used for increasing the success rate of classification based on the artificial neural network algorithm. The gravity centers of matched conjugate planes are regarded as tie points for strip adjustments in this study. The advantage of the current algorithm is that the choice of tie points can be executed automatically. The results of experiments of strip adjustments show the feasibility of our algorithm to improve the height accuracy of airborne Lidar data.