Applicability Verification of RFM Model in Aerial Remote Sensing Image Optimization

doi:10.7517/j.issn.1674-0475.2018.02.003

Imaging Science and Photochemistry ›› 2018, Vol. 36 ›› Issue (2): 137-148.DOI: 10.7517/j.issn.1674-0475.2018.02.003

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Applicability Verification of RFM Model in Aerial Remote Sensing Image Optimization

XU Chunxue¹, CHEN Rui², ZHAO Hongying²

1. Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, P. R. China;
2. Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, P. R. China

Received:2017-03-08 Revised:2017-04-12 Online:2018-03-15 Published:2018-03-15

Abstract

Abstract: Aiming at the limitation of the practical application situation based on the collinear equation, the rational function model (RFM) for aerial image resolution and the effectiveness of its solution in aerial image are studied and explored. The RFM is given a systematic explanation from the aspects of the definition and characteristic of the model and its solution. The fitting accuracy between RFM and classic bundle adjustment is also analyzed. Based on the triangulation measurement results of collinearity method, the experimental result proves the good performance of RFM compared with classic collinearity method. The results show that the rational function model can fit the bundle adjustment with high precision, and it can replace the classical model that based on the strict collinear equation to complete the task of aerial triangulation, thus providing a theoretical reference for the application of RFM in close-range images such as unmanned aerial vehicles.

Key words: collinearity equation, rational function model (RFM), regularization, aerial images

XU Chunxue, CHEN Rui, ZHAO Hongying. Applicability Verification of RFM Model in Aerial Remote Sensing Image Optimization[J]. Imaging Science and Photochemistry, 2018, 36(2): 137-148.

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Applicability Verification of RFM Model in Aerial Remote Sensing Image Optimization

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