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RPC Correction Coefficient Extrapolation for KOMPSAT-3A Imagery in Inaccessible Regions.

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Title: RPC Correction Coefficient Extrapolation for KOMPSAT-3A Imagery in Inaccessible Regions.
Authors: Kim, Namhoon1 (AUTHOR)
Source: Remote Sensing. Oct2025, Vol. 17 Issue 19, p3332. 27p.
Subjects: Extrapolation, Calibration, Relief models, Geospatial data, Artificial satellites
Abstract: Highlights: What are the main findings? This study proposes a transport-based RPC correction learned on a small head subset, extrapolating downstream while preserving geometry and yielding <3-pixel tails in two of three strips. This study models pushbroom error extrapolation by leveraging satellite orbital parameters and terrain characteristics to transport head-of-strip corrections downstream. What is the implication of the main finding? This study provides a practical alternative to strip-wide block adjustment for control-denied or resource-limited settings, operating in image space and tolerating missing segments or ties. This study offers a transferable framework for sub-meter platforms; under stronger dynamics, broader calibration and optional higher-order terms further stabilize transport. High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors' rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to <2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments. [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: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Remote+Sensing%22&quot;&gt;Remote Sensing&lt;/searchLink&gt;. Oct2025, Vol. 17 Issue 19, p3332. 27p.
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  Data: Highlights: What are the main findings? This study proposes a transport-based RPC correction learned on a small head subset, extrapolating downstream while preserving geometry and yielding &lt;3-pixel tails in two of three strips. This study models pushbroom error extrapolation by leveraging satellite orbital parameters and terrain characteristics to transport head-of-strip corrections downstream. What is the implication of the main finding? This study provides a practical alternative to strip-wide block adjustment for control-denied or resource-limited settings, operating in image space and tolerating missing segments or ties. This study offers a transferable framework for sub-meter platforms; under stronger dynamics, broader calibration and optional higher-order terms further stabilize transport. High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors&#39; rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to &lt;2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;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&#39;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.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17193332
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 27
        StartPage: 3332
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      – SubjectFull: Extrapolation
        Type: general
      – SubjectFull: Calibration
        Type: general
      – SubjectFull: Relief models
        Type: general
      – SubjectFull: Geospatial data
        Type: general
      – SubjectFull: Artificial satellites
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      – TitleFull: RPC Correction Coefficient Extrapolation for KOMPSAT-3A Imagery in Inaccessible Regions.
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            NameFull: Kim, Namhoon
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            – D: 01
              M: 10
              Text: Oct2025
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              Y: 2025
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