Algorithms research of airborne long linear multi-elements whisk broom remote sensing image geometric correction

Bin Xu; Yan-hua Ma; Sheng-hong Li

doi:10.1117/12.2202942

8 October 2015 Algorithms research of airborne long linear multi-elements whisk broom remote sensing image geometric correction

Bin Xu, Yan-hua Ma, Sheng-hong Li

Author Affiliations +

Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96752Y (2015) https://doi.org/10.1117/12.2202942
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

Multi-Element scanning imaging is an imaging method that is conventionally used in space-born spectrometer. By multipixel scanning at the same time, increased exposure time can be achieved and the picture quality can be enhanced. But when this imaging method is applied in airborne remote sensing image systems, corresponding imaging model and correction algorithms must be built, because of the poor posture stability of airborne platform and different characteristics and requirements. This paper builds a geometric correction model of airborne long linear multi-element scanning imaging system by decomposing the process of imaging and also deduced related correction algorithms. The sampling moment of linear CCD can be treated as push broom imaging and a single pixel imaging during the whole whisk broom period can be treated as whisk broom imaging. Based on this kind of decomposition, col-linearity equation correction algorithm and a kind of new tangent correction algorithm are deduced. As shown in the simulation experiment result, combining with position and attitude date collected by the posture position measurement system, these algorithms can map pixel position from image coordinate to WGS84 coordinate with high precision. In addition, some error factors and correction accuracy are roughly analyzed.

Citation Download Citation

Bin Xu, Yan-hua Ma, and Sheng-hong Li "Algorithms research of airborne long linear multi-elements whisk broom remote sensing image geometric correction", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96752Y (8 October 2015); https://doi.org/10.1117/12.2202942

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