空載光達的系統性誤差會造成點位高程坐標偏移，而航帶平差是一種消除系統性誤差的方法，此方法藉由重疊航帶對應區域或連結點應具有相同高程的特性來求解每條航帶的變形參數。使用此方法時，對應區域或連結點的位置通常需要由人工選取與決定。

為了自動找出重疊航帶的對應區域(特別是平面區塊)，本研究使用張量投票法來偵測光達平面。此外，不同航帶平面彼此間的位相關係一般而言不因系統誤差而有較大的變化。應用此特性，本研究將具有相似位相關係的平面以類神經網路進行匹配，最後再將匹配後得到的共軛平面重心坐標視為連結點進行航帶平差。使用此方法的好處是連結點的選取工作可以自動化地執行。同時於航帶平差實驗中，本研究探討了交叉航帶資料、連結點與控制點的分佈與數量對航帶平差結果的影響。實驗結果顯示，本文所提出的方法對於改善空載光達的高程精度是可行的。

The systematic errors of airborne Lidar cause elevation offset of point clouds. Strip adjustment is one of the ways to reduce systematic errors. Using strip adjustment, the parameters of deformation in each strip can be solved by means of the corresponding blocks or tie points in overlapped strips. Nevertheless, the location of corresponding blocks or tie points usually be selected and decided manually.

In order to find out corresponding blocks, especially planar blocks, in overlapped strips automatically, a tensor voting algorithm is presented for detecting planes from Lidar data in this article. In general, the topology of planes in strips may be similar even if systematic errors exist. In the research an artificial neural network method is adopted for matching the planes with similar topology. The centre of gravity of matched conjugate planes will be regarded as tie points for strip adjustments. The advantage of this algorithm is that the choice of tie points can be executed automatically. In experiments of strip adjustments, this research discusses the influence of cross strips, distribution and the number of tie points and control points. The results of our experiments show the feasibility of our algorithm to improve the accuracy of heights by airborne Lidar data.

王奕鈞 (2006)。”神經網路應用於地籍坐標轉換之研究”。國立政治大學地政學系研究所碩士論文。
尤瑞哲和游勳喬 （2003）。”空載雷射掃瞄安置角自動化率定”。航測及遙測學刊論文，8(3)：15-32。
何心瑜 (2006)。”空載光達作業流程及品質管理之研究”。國立交通大學土木工程研究所碩士論文。
呂維哲 (2002)。”模糊類神經網路為架構之遙測影像分類器設計”。國立中央大學資訊工程研究所碩士論文。
李德仁 (1988)。誤差處理和可靠性理論。測繪出版社。北京。
周富晨 (2004)。”適應性點雲過濾演算法於空載光達資料產生數值高程模型之研究”。國立成功大學測量及空間資訊研究所碩士論文。
施永富　(2002)。測量學。三民書局股份有限公司。台北。
徐百輝 (2005)。”小波神經網路於高光譜影像分類之研究”。第廿四屆測量學術及應用研討會論文集。
莊智清和黃國興　(2001)。電子導航。全華科技圖書股份有限公司。
葉怡成 (2003)。類神經網路模式應用與實作。儒林出版社。台北。
童俊雄 (2005)。”空載光達系統誤差分析與航帶平差方法之探討”。國立成功大學測量及空間資訊學系碩士論文。
劉榮寬、徐偉城、史天元、劉進金 (2005)。”空載光達系統率定初探”。第廿四屆測量學術及應用研討會論文集一，pp.447-456。
蔡欣怡 (2004)。”結合雷射測高與強度資料進行區域平差可行性之研究”。國立成功大學測量及空間資訊學系碩士論文。
羅華強 (2001)。類神經網路-MATLAB的應用。清蔚科技股份有限公司。台北。
Barsi, A. (2001). “Performing coordinate transformation by artificial neural network.” AVN,2001(4):134-137.
Burman, H. (2000). “Adjustment of Laser Scanner Data for Correction of Orientation Errors.” International Archives of Photogrammetry and Remote Sensing, Amsterdam,125-132.
Crombaghs, M.J.E., Bruegelmann, R., and de Min, E.J. (2000). “On the Adjustment of Overlapping Strips of Laser altimeter Height Data.” International Archives of Photogrammetry and Remote Sensing, Amsterdam,230-237.
Duckham, M. and Worboys, M. (2004). GIS: A Computing Perspective. CRC Press.
Filin, S. (2002). “Surface clustering from airborne laser scanning data.” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIV, 3A, Graz,117-124.
Kilian, J., Haala, N., and Englich, M. (1996). “Capture and Evaluation of Airborne Laser Scanner Data.” International Archives of Photogrammetry and Remote Sensing, Vienna,383-388.
Maas, H.G. and Vosselman, G. (1999). “Two algorithms for extracting building models from raw laser altimetry data.” ISPRS Journal of Photogrammetry and Remote, Sensing 54 (2-3),153-163.
Medioni, G., Lee, M.S., and Tang, C.K. (2000). A Computational Framework for Segmentation and Groupin., Elsevier Science, Amsterdam.
Roggero, M. (2002). “Object segmentation with region growing and principal components analysis.” Proceeding of ISPRS Commission III, Symposium on Photogrammetric Computer Vision, part A, Graz, Austria, 9-13 Sept, 289-294.
Schenk, T. (2001). “Modeling and Recovering Systematic Errors in Airborne Laser Scanners.” OEEPE Workshop on Airborne Laser scanning and Interferometric SAR for Detailed Digital Elevation Models, Stockholm,40-48.
Schuster, H.F. (2004). “Segmentation of lidar data using the tensor voting framework.” in XXth ISPRS Congress: Geo-Imagery Bridging Continents, Commission III, volume Vol.35 part B3.
Specht, D.F. (1990). “Probabilistic neural network.”Neural Networks,3,109-118.
Vosselman, G. (2000). “Slope Based Filtering of Laser Altimetry Data.” International Archives of Photogrammetry and Remote Sensing, Vol.33, part B3, Amsterdam,935-942.
Vosselman, G. and Maas, H.G. (2001). “Adjustment and Filtering of Raw Laser Altimetry Data.” OEEPE Workshop on Airborne Laser scanning and Interferometric SAR for Detailed Digital Elevation Models, Stockholm,62-73.
Wehr, A. and Lohr, U. (1999). “Airborne laser scanning - an introduction and overview.” ISPRS Journal of Photogrammetry & Remote Sensing, 54: 68-82.
You, R.J. and Lin B.C. (2007). “Building feature extraction from airborne lidar data using tensor voting algorithm.” ISPRS J.Photogrammetry and Remote Sensing (in review).