A novel inverse transformation algorithm for pushbroom TDI CCD imaging. Issue 3 (1st February 2022)

• Record Type: Journal Article
• Authors: Jiang, Yonghua; Zhang, Guo; Li, Litao; Tan, Ziqiang; Tang, Zhongxing; Xu, Yi
• Is Part Of: International journal of remote sensing. Volume 43:Issue 3(2022)
• Page Start: 1074
• Page End: 1090
• Publication Date: 2022-02-01
• Subjects: TDI CCD -- curve imaging -- rational function model -- collinearity equation -- inverse transformation; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2022.2032459
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Pushbroom time delay integration charge-coupled device imaging is the primary imaging mode of high-resolution satellites. This imaging mode requires an inverse transformation to determine image coordinates from ground coordinates relying on an iterative algorithm. This process is complicated and time-consuming, and convergence may fail. With the improvement of the agility and mobility of optical satellites, curve scanning imaging becomes possible, which will be the dominant imaging mode to greatly improve the observation efficiency of satellite in the future. However, the traditional inverse transformation algorithm is no longer applicable to curve imaging mode. Based on the essential characteristics of pushbroom time delay integration charge-coupled device imaging, we propose a novel inverse transformation algorithm with approximate perpendicularity constraint between CCD array and image motion trajectory. This algorithm can not only be applied to both traditional push-scan mode and curve imaging mode, but also improve the computational efficiency of inverse transformation. In this paper, the simulation data of curve imaging mode and traditional push-scan imaging data of JiLin-01, GaoFen26A and GaoFen26B are used to carry out experiments. Compared with the existing inverse transformation algorithm, the proposed method can be well applied to the curve imaging mode. Moreover, the inverse transformation accuracy can reach 0.0002–0.0006 pixels and the computational efficiency is improved by 550% compared with affine model and 35.6% compared with scanning plane model for traditional push-scan imaging, which verifies the correctness and feasibility of the proposed algorithm.