使用十二生肖光进行星载偏振成像仪校准。

IF 20.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2023-07-27 DOI:10.1109/TPAMI.2023.3299526
Or Avitan, Yoav Y Schechner, Ehud Behar
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引用次数: 0

摘要

我们提出,星载偏振成像仪可以使用黄道光(ZL)进行校准或自校准。ZL是由一团行星际尘埃粒子形成的。它在宽视场中具有显著的偏振度。从太空看,ZL不受地面干扰的影响。ZL对相机位置不敏感,适合于卫星星座的同时交叉校准。ZL以月为单位变化,因此在实际校准过程中是一个准恒定目标。我们推导了极化成像的前向模型。在此基础上,我们提出了极化校准和自校准的逆问题,并给出了求解算法。这里的方法在模拟中得到了演示。为了进行这些模拟,我们绘制了天空的偏振图像,包括来自太空的ZL、偏振扰动和成像噪声。
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Using Zodiacal Light For Spaceborne Calibration Of Polarimetric Imagers.

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realistic calibration sessions. We derive a forward model for polarimetric image formation. Based on it, we formulate an inverse problem for polarimetric calibration and self-calibration, as well as an algorithm for the solution. The methods here are demonstrated in simulations. Towards these simulations, we render polarized images of the sky, including ZL from space, polarimetric disturbances, and imaging noise.

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来源期刊
CiteScore
28.40
自引率
3.00%
发文量
885
审稿时长
8.5 months
期刊介绍: The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.
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