光谱学与光谱分析 |
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Simulation Analysis & Experimental Study of the Effects of Satellite Vibration Frequency and Amplitude on Hyperspectral Image |
NAN Yi-bing, NI Guo-qiang* |
School of Optoelectronics, Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,Beijing Institute of Technology, Beijing 100081, China |
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Abstract In the imaging process of satellite-based pushbroom hyperspectral imager, attitude motions of satellite platform, represented by vibrations, will cause aliasing of the object information comes from different sub-areas of the detector, leading to degradation of hyperspectral image quality. In order to suppress and correct the imaging errors caused by satellite vibrations more effectively, spatial and spectral degradation mechanisms of typical dispersive pushbroom imaging spectrometer caused by satellite vibration are studied in this paper, including theoretical simulation and experimental study. With the analysis of spectral mixing process during exposure, the relationship between spectrum of ground object and satellite attitude is obtained, and a degradation model of pushbroom spectral imaging is presented. The effects of different attitudes of vibration are considered in the degradation model. Mean mixing ratios of each pixel are easy to calculate with a universal coefficient matrix, as long as the satellite attitude parameters of each moment are known. Then the simulation degraded spectral image data cube is achieved. The common expression of mean mixing ration is derived in detail. More important, the effects of vibration amplitude and frequency are quantitative analyzed separately. Degraded simulation and ground simulation experiments are carried out based on real hyperspectral data cubes, then the quality of the cubes before and after degradation are evaluated. Results show that simulation is in good agreement with reality. Mean mixing ratio can reflect the degradation extent of hyperspectral data directly. The satellite vibrations bring about spatial quality deteriorate of hyperspectral image, and lead to the aliasing of spectrum comes from different ground object. The degradation extent of hyperspectral data is determined mainly by vibration amplitude. The influence of frequency is limited.
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Received: 2015-07-01
Accepted: 2015-11-18
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Corresponding Authors:
NI Guo-qiang
E-mail: nigq@bita.org.cn
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[1] Yuan Y,Zhang X,Sun C,et al. Optik,2011,122(17):1576. [2] YU Cheng-wei, CHEN De-rong, YANG Jian-feng, et al(余成伟,谌德荣,杨建峰,等). Opto-Electronic Engineering(光电工程),2004,31(5):4. [3] ZHAO Hui-jie, JIA Guo-rui(赵慧洁,贾国瑞). Acta Optica Sinica(光学学报),2008,28(3):583. [4] TANG Qiu-yan, TANG Yi, CAO Wei-liang, et al(唐秋艳,唐 义,曹玮亮,等). Acta Physica Sinica(物理学报),2012,61(7):6. [5] Wang X,Yi T. Influence of Satellite Attitude Jitter on Dispersive Spectrometer Imaging Offset. Information. Engineering and Computer Science,2009. ICIECS 2009. International Conference on. IEEE,2009. 1. [6] Lee S,Alexander J W,Jeganathan M. Proceedings of SPIE-The International Society for Optical Engineering,2000,3932. [7] Xu P,Hao Q,Huang C,et al. Degradation of Image Quality Caused by Vibration in Push-Broom Camera. Photonics Asia 2002. International Society for Optics and Photonics,2002. [8] Lieber M D. Space-Based Optical System Performance Evaluation with Integrated Modeling Tools. Defense and Security. International Society for Optics and Photonics,2004. [9] Kramer H J. Observation of the Earth and Its Environment:Survey of Missions and Sensors. Springer,2002. [10] BIAN Zhi-qiang, CAI Chen-sheng, Lü Wang, et al(边志强,蔡陈生,吕 旺,等). Aerospace Shanghai(上海航天),2014,31(3):24. [11] ZHANG Xiu-bao, YUAN Yan, WANG Qian(张修宝,袁 艳,王 潜). Acta Optica Sinica(光学学报),2011,31(5):244. |
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