Error analysis and optimal design of reduction relay lens for field of view stitching applications

Sensor Review ◽  
2021 ◽  
Vol 41 (1) ◽  
pp. 87-92
Author(s):  
Xinjie Zhang ◽  
Fansen Kong ◽  
Zhiyuan Gu ◽  
Xiao Shen
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
High Accuracy ◽  
Structure Design ◽  
Reduction Ratio ◽  
Field Of View ◽  
Machining Accuracy ◽  
Optical Remote Sensing ◽  
Optical Elements ◽  
Content Type

Purpose FOV splicing optical remote sensing instruments have a strict requirement for the focal length consistency of the lens. In conventional optical-mechanical structure design, each optical element is equally distributed with high accuracy and everyone must have a high machining and assembly accuracy. For optical remote sensors with a large number of optical elements, this design brings great difficulties to lens manufacture and alignment. Design/methodology/approach Taking the relay lens in an optical remote sensing instrument with the field of view splicing as an example, errors of the system are redistributed to optical elements. Two optical elements, which have the greatest influence on modulation transfer function (MTF) of the system are mounted with high accuracy centering and the other elements are fixed by gland ring with common machining accuracy. The reduction ratio consistency difference among lenses is compensated by adjusting the optical spacing between the two elements. Findings Based on optical system simulation analysis, the optimized structure can compensate for the difference of reduction ratio among lens by grinding the washer thickness in the range of ±0.37 mm. The test data for the image quality of the lens show that the MTF value declined 0.043 within ±0.4 mm of space change between two barrels. The results indicate that the reduction ratio can be corrected by adjusting the washer thickness and the image quality will not obviously decline. Originality/value This paper confirms that this work is original and has not been published elsewhere nor is it currently under consideration for publication elsewhere. In this paper, the optimum structural design of the reduction relay lens for the field of view stitching applications is reported. The method of adjusting washer thickness is applied to compensate for the reduction ratio consistency difference of lenses. The optimized structure also greatly reduces the difficulty of lenses manufacture, alignment and improves the efficiency of assembly.

On-Orbit Calibration Approach Based on Partial Calibration-Field Coverage for the GF-1/WFV Camera

2019 ◽  
Vol 85 (11) ◽  
pp. 815-827 ◽  
Author(s):  
Mi Wang ◽  
Beibei Guo ◽  
Ying Zhu ◽  
Yufeng Cheng ◽  
Chenhui Nie
Keyword(s):  
High Resolution ◽  
High Accuracy ◽  
Field Of View ◽  
Estimation Methods ◽  
Large Field ◽  
Wide Field ◽  
Optical Remote Sensing ◽  
Calibration Data ◽  
Stereoscopic Images ◽  
Wide Field Of View

The Gaofen-1 (GF1) optical remote sensing satellite is the first in China's series of high-resolution civilian satellites and is equipped with four wide-field-of-view cameras. The cameras work together to obtain an image 800 km wide, with a resolution of 16 m, allowing GF1 to complete a global scan in four days. To achieve high-accuracy calibration of the wide-field-of-view cameras on GF1, the calibration field should have high resolution and broad coverage based on the traditional calibration method. In this study, a GF self-calibration scheme was developed. It uses partial reference calibration data covering the selected primary charge-coupled device to achieve high-accuracy calibration of the whole image. Based on the absolute constraint of the ground control points and the relative constraint of the tie points of stereoscopic images, we present two geometric calibration models based on paired stereoscopic images and three stereoscopic images for wide-field-of-view cameras on GF1, along with corresponding stepwise internal-parameter estimation methods. Our experimental results indicate that the internal relative accuracy can be guaranteed after calibration. This article provides a new approach that enables large-field-of-view optical satellites to achieve high-accuracy calibration based on partial calibration-field coverage.

scholarly journals Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

2020 ◽  
Vol 12 (24) ◽  
pp. 4029
Author(s):  
Sakib Kabir ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Quality Parameters ◽  
Optical Remote Sensing ◽  
Coherent Noise ◽  
Ground Sample ◽  
Characterization Methods ◽  
Remote Sensing Satellite ◽  
Improved Technology

Over the past decade, number of optical Earth-observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lack an on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with their specifications or requirements. In cases of multiple methods, recommendations have been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that need to be assessed to evaluate remote sensing satellite data quality, and the best vicarious methodologies to evaluate identified quality parameters such as coherent noise and ground sample distance.

Study Method for Testing Image Quality of Optical Remote Sensing Satellite of Vietnam

2018 ◽  
Vol 2 (9) ◽  
Author(s):  
Nghiem Van Tuan ◽  
◽  
Nguyen Minh Ngoc ◽  
Tran Van Anh ◽  
Do Thi Phuong Thao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Optical Remote Sensing ◽  
Remote Sensing Satellite

An High Accuracy Image Mosaicking Approach of Optical Remote Sensing Satellite for Multi-Camera System

2017 ◽  
Vol 37 (8) ◽  
pp. 0828003
Author(s):  
程宇峰 Cheng Yufeng ◽  
金淑英 Jin Shuying ◽  
王 密 Wang Mi ◽  
常学立 Chang Xueli ◽  
朱 映 Zhu Ying
Keyword(s):  
Remote Sensing ◽  
High Accuracy ◽  
Optical Remote Sensing ◽  
Camera System ◽  
Image Mosaicking ◽  
Remote Sensing Satellite

scholarly journals Remote Sensing Image Compression Evaluation Method Based on Neural Network Prediction and Fusion Quality Fidelity

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenbing Yang ◽  
Feng Tong ◽  
Xiaoqi Gao ◽  
Chunlei Zhang ◽  
Guantian Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Quality ◽  
Evaluation Method ◽  
Retention Rate ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Compression Efficiency ◽  
Neural Network Prediction ◽  
Network Prediction

Lossy compression can produce false information, such as blockiness, noise, ringing, ghosting, aliasing, and blurring. This paper provides a comprehensive model for optical remote sensing image characteristics based on the block standard deviation’s retention rate (BSV). We first propose a compression evaluation method, CR_CI, that combines neural network prediction and remote sensing image quality fidelity. Through the compression evaluation and improved experimental verification of multiple satellites (CBERS-02B satellite, ZY-1-02C satellite, CBERS-04 satellite, GF-1, GF-2, etc.), CR_CI can be stable, cleverly test changes in the information extraction performance of optical remote sensing images, and provide strong support for optimizing the design of compression schemes. In addition, a predictor of remote sensing image number compression is constructed based on deep neural networks, which combines compression efficiency (compression ratio), image quality, and protection. Empirical results demonstrate the image’s highest compression efficiency under the premise of satisfying visual interpretation and quantitative application.

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