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A Single-Transformation Model for Fisheye Image Orthorectification.

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Title: A Single-Transformation Model for Fisheye Image Orthorectification.
Authors: Wang, Qingyang1,2,3 (AUTHOR), Zhou, Guoqing1,2 (AUTHOR) gzhou@glut.edu.cn, Yue, Tao1,3 (AUTHOR), Song, Bo1 (AUTHOR), Jiang, Jianwu1,2,3 (AUTHOR), Cao, Zhen3 (AUTHOR), Zhang, Xing3 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1651. 28p.
Subjects: Spherical geometry, Spherical projection, Digital elevation models
Abstract: Highlights: What are the main findings? A single-transformation orthorectification model for fisheye imagery is developed based on spherical geometry and equidistant projection. The method achieves consistent sub-decimeter accuracy across DSLR and UAV fisheye datasets. What are the implications of the main findings? The proposed model provides a solution for accurate orthorectification of fisheye imagery in both ground-based and UAV remote sensing applications. The study reveals the impact of peripheral distortion on accuracy, highlighting the need for improved correction strategies in edge regions for complete fisheye image utilization. Fisheye lenses can capture surrounding spatial information at once, making them widely applied in various fields. However, the imaging principle of fisheye lenses does not satisfy the collinearity equation, so the theory of orthorectification using traditional differential orthorectification is no longer applicable for a fisheye image in practice. Therefore, this paper develops a single-spherical-geometry-transformation model for fisheye image orthorectification. This model directly establishes the relationship between spatial ground points and image plane coordinates through spherical geometry, and then combines the digital surface model (DSM) to correct points in the fisheye image to their correct positions on a pixel-by-pixel basis, thereby achieving fisheye image orthorectification. To validate the feasibility of the proposed orthorectification model, an indoor calibration field was established. Experimental validation was then conducted using two fisheye image datasets: an indoor dataset acquired in the calibration field with a digital single-lens reflex (DSLR) camera and an outdoor dataset acquired with an unmanned aerial vehicle (UAV). The results of the two groups of experiments demonstrate that the proposed model can effectively orthorectify fisheye images with ground accuracies of 0.055 m and 0.097 m in x and y direction, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Single-Transformation Model for Fisheye Image Orthorectification.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qingyang%22">Wang, Qingyang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Guoqing%22">Zhou, Guoqing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gzhou@glut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yue%2C+Tao%22">Yue, Tao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Bo%22">Song, Bo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Jianwu%22">Jiang, Jianwu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Zhen%22">Cao, Zhen</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xing%22">Zhang, Xing</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: Highlights: What are the main findings? A single-transformation orthorectification model for fisheye imagery is developed based on spherical geometry and equidistant projection. The method achieves consistent sub-decimeter accuracy across DSLR and UAV fisheye datasets. What are the implications of the main findings? The proposed model provides a solution for accurate orthorectification of fisheye imagery in both ground-based and UAV remote sensing applications. The study reveals the impact of peripheral distortion on accuracy, highlighting the need for improved correction strategies in edge regions for complete fisheye image utilization. Fisheye lenses can capture surrounding spatial information at once, making them widely applied in various fields. However, the imaging principle of fisheye lenses does not satisfy the collinearity equation, so the theory of orthorectification using traditional differential orthorectification is no longer applicable for a fisheye image in practice. Therefore, this paper develops a single-spherical-geometry-transformation model for fisheye image orthorectification. This model directly establishes the relationship between spatial ground points and image plane coordinates through spherical geometry, and then combines the digital surface model (DSM) to correct points in the fisheye image to their correct positions on a pixel-by-pixel basis, thereby achieving fisheye image orthorectification. To validate the feasibility of the proposed orthorectification model, an indoor calibration field was established. Experimental validation was then conducted using two fisheye image datasets: an indoor dataset acquired in the calibration field with a digital single-lens reflex (DSLR) camera and an outdoor dataset acquired with an unmanned aerial vehicle (UAV). The results of the two groups of experiments demonstrate that the proposed model can effectively orthorectify fisheye images with ground accuracies of 0.055 m and 0.097 m in x and y direction, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2026
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