Linear solution of overdeterminated seven-parameter transformation

Ta Tzu Tsai, Rey Jer You

研究成果: Paper同行評審


The current applications in Photogrammetry and mobile mapping systems (MMSs) use normally the centric perspective projection to construct the mathematic relationship of images and their corresponding ground points. The relationship is based on a three dimensional seven-parameter similarity transformation which is generally nonlinear. Traditionally, to solve these kinds of transformation problems, we have to first use the linearization method and give the initial values of unknowns together with iterative processing to solve the transformation parameters. Linearized solution procedures need relative good initial values of orientation elements and approximated ground coordinates of object points which may lead to some instable problems and computation-effectiveness. In this paper, we transform the nonlinear seven-parameter similarity transformation to a linear one by a special transformation, namely the Cayley transformation. In this model, we do not need the initial values and linearization. The solution procedures of the linear model here will be suggested by three step stages. The algebra formulation will be given. In this paper, a set of three coordinates is simulated with random errors for analysis. The results of the linear method including Helmert method and Molodensky method will be compared with those of the iteratively linearized method. Finally, we will discuss the application possibility of the linear model for Photogrammetry and MMSs.

出版狀態Published - 2018 1月 1
事件39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 - Kuala Lumpur, Malaysia
持續時間: 2018 10月 152018 10月 19


Conference39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018
城市Kuala Lumpur

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 資訊系統
  • 地球與行星科學(全部)
  • 電腦網路與通信


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