Geometric correction of optical remote sensing satellite images captured by linear array sensors circular scanning perpendicular to the orbit

2021 ◽  
Vol 29 (12) ◽  
pp. 2924-2934
Author(s):  
Wu XUE ◽  
◽  
Peng WANG ◽  
Ling-yu ZHONG
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Linear Array ◽  
Optical Remote Sensing ◽  
Geometric Correction ◽  
Remote Sensing Satellite

scholarly journals Assessment of geometric correction of remote sensing satellite images using RPC versus GCP

2018 ◽  
Vol 2 (2) ◽  
pp. 82-89
Author(s):  
Mohamed Soliman ◽  
Hassan Hamza ◽  
Hassan Elhifnawy ◽  
Ayman Ragab ◽  
Ahmed Elsharkawy
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Geometric Correction ◽  
Remote Sensing Satellite

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.

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