The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 305–312, 2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 305–312, 2016

  07 Jun 2016

07 Jun 2016


Weili Jiao, Tengfei Long, Saiguang Ling, and Guojin He Weili Jiao et al.
  • Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China

Keywords: uncertainty, remote sensing, geometric correction, image quality, error model visualization

Abstract. It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.