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Error-correct arithmetic for angular displacement measurement with single linear image detector 期刊论文  OAI收割
Optical Engineering, 2018, 卷号: 57, 期号: 5, 页码: 4
作者:  
Yu, H.;  Wan, Q. H.;  Lu, X. R.;  Du, Y. C.;  Zhao, C. H.
  |  收藏  |  浏览/下载:23/0  |  提交时间:2019/09/17
Measuring the system gain of the TDI CCD remote sensing camera (EI CONFERENCE) 会议论文  OAI收割
Advanced Materials and Devices for Sensing and Imaging II, November 8, 2004 - November 10, 2004, Beijing, China
Ya-xia L.; Hai-ming B.; Jie L.; Jin R.; Zhi-hang H.
收藏  |  浏览/下载:62/0  |  提交时间:2013/03/25
The gain of a TDI CCD camera is the conversion between the number of electrons recorded by the TDI CCD and the number of digital units (counts) contained in the CCD image"[1]. TDI CCD camera has been a main technical approach for meeting the requirements of high-resolution and lightweight of remote sensing equipment. It is useful to know this conversion for evaluating the performance of the TDI CCD camera. In general  a lower gain is better. However  the resulting slope is the gain of the TDI CCD. We did the experiments using the Integration Sphere in order to get a flat field effects. We calculated the gain of the four IT-EI-2048 TDI CCD. The results and figures of the four TDI CCD are given.  this is only true as long as the total well depth (number of electrons that a pixel can hold) of the pixels can be represented. High gains result in higher digitization noise. System gains are designed to be a compromise between the extremes of high digitization noise and loss of well depth. In this paper  the mathematical theory is given behind the gain calculation on a TDI CCD camera and shows how the mathematics suggests ways to measure the gain accurately according to the Axiom Tech. The gains were computed using the mean-variance method  also known as the method of photon transfer curves. This method uses the effect of quantization on the variance in the measured counts over a uniformly illuminated patch of the detector. This derivation uses the concepts of signal and noise. A linear fit is done of variance vs. mean