张浩在《Remote Sensing》上发表文章 “Exterior Orientation Parameter Refinement of the First Chinese Airborne Three-Line Scanner Mapping System AMS-3000”
Cite:
Zhang H, Duan Y, Qin W, Zhou Q, Zhang Z. Exterior Orientation Parameter Refinement of the First Chinese Airborne Three-Line Scanner Mapping System AMS-3000. Remote Sensing. 2024; 16(13):2362. https://doi.org/10.3390/rs16132362
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Open AccessArticle
Exterior Orientation Parameter Refinement of the First Chinese Airborne Three-Line Scanner Mapping System AMS-3000
by
Hao Zhang
,Yansong Duan
*
,Wei Qin
,Qi Zhou
andZuxun Zhang
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Submission received: 23 May 2024 / Revised: 20 June 2024 / Accepted: 25 June 2024 / Published: 27 June 2024
Abstract
The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features.