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From Optical Design to NIIRS and Object Detection: An Integrated Framework for Spatial Image Quality Assessment of Micro-Satellite Constellations.

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Title: From Optical Design to NIIRS and Object Detection: An Integrated Framework for Spatial Image Quality Assessment of Micro-Satellite Constellations.
Authors: Yoon, Jisang1 (AUTHOR) jisangyoon@kaist.ac.kr, Lee, Junchan1,2 (AUTHOR), Lee, Suwon1,3 (AUTHOR), Jang, Gilsun2,4 (AUTHOR), Park, Jueon1,3 (AUTHOR), Jeon, Woojin1,2 (AUTHOR), Lee, Sang-Hyun1,3 (AUTHOR), Lee, Chol1,4 (AUTHOR), Lim, Cheol-Woo1 (AUTHOR), Oh, Chi-Wook1 (AUTHOR), Kim, Se-Yon1 (AUTHOR), Park, Seong-Ook4 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1943. 32p.
Subjects: Image quality analysis, Nanosatellites, Image processing, Remote-sensing images, Optical engineering, Object recognition (Computer vision), Spatial resolution
Abstract: Highlights: What are the main findings? An integrated framework was established to connect optical design, NIIRS, and object detection using NEONSAT-1 imagery. In the NEONSAT-1 experiments, system MTF and simulated GSD changes were reflected in NIIRS, while detection response varied by scene, product type, and target class. What are the implications of the main findings? NIIRS is useful for estimating detection potential, but it should not be used as a single universal indicator for all target classes. The proposed framework provides a baseline for future image quality assessment across a micro-satellite constellation. For micro-satellite constellations, frequent Earth observation alone does not guarantee archive usability; the archive is operationally useful only when the spatial image quality remains adequate for downstream exploitation. This study presents an integrated framework for assessing spatial image quality using NEONSAT-1 imagery by linking optical design analysis, image simulation, GIQE-based NIIRS estimation, and YOLOv8-based object detection within a single workflow. NEONSAT-1 panchromatic (PAN), pan-sharpened (PS), and multispectral (MS) products were analyzed together with controlled simulations of system MTF, altitude-dependent GSD variation, and super-resolution processing. Among the native products, PS imagery showed the highest NIIRS and overall detection performance. In the controlled experiments, higher system MTF increased RER and NIIRS, while lower simulated altitude generally produced finer GSD and higher NIIRS for both PS and PAN products. However, detection performance varied by scene, product type, and target class and did not increase in direct proportion to NIIRS. In the super-resolution case study, ×2 SR provided the most consistent NIIRS improvement, whereas detection responses at higher SR scales were target class dependent. These results suggest that spatial image quality should be evaluated not only through interpretability metrics such as NIIRS but also in relation to practical downstream performance. The proposed framework provides a baseline for future constellation-scale image quality assessment. [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: From Optical Design to NIIRS and Object Detection: An Integrated Framework for Spatial Image Quality Assessment of Micro-Satellite Constellations.
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  Data: <searchLink fieldCode="AR" term="%22Yoon%2C+Jisang%22">Yoon, Jisang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jisangyoon@kaist.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Junchan%22">Lee, Junchan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Suwon%22">Lee, Suwon</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jang%2C+Gilsun%22">Jang, Gilsun</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Jueon%22">Park, Jueon</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jeon%2C+Woojin%22">Jeon, Woojin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Sang-Hyun%22">Lee, Sang-Hyun</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Chol%22">Lee, Chol</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lim%2C+Cheol-Woo%22">Lim, Cheol-Woo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Oh%2C+Chi-Wook%22">Oh, Chi-Wook</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Se-Yon%22">Kim, Se-Yon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Seong-Ook%22">Park, Seong-Ook</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1943. 32p.
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  Data: <searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Nanosatellites%22">Nanosatellites</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+engineering%22">Optical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? An integrated framework was established to connect optical design, NIIRS, and object detection using NEONSAT-1 imagery. In the NEONSAT-1 experiments, system MTF and simulated GSD changes were reflected in NIIRS, while detection response varied by scene, product type, and target class. What are the implications of the main findings? NIIRS is useful for estimating detection potential, but it should not be used as a single universal indicator for all target classes. The proposed framework provides a baseline for future image quality assessment across a micro-satellite constellation. For micro-satellite constellations, frequent Earth observation alone does not guarantee archive usability; the archive is operationally useful only when the spatial image quality remains adequate for downstream exploitation. This study presents an integrated framework for assessing spatial image quality using NEONSAT-1 imagery by linking optical design analysis, image simulation, GIQE-based NIIRS estimation, and YOLOv8-based object detection within a single workflow. NEONSAT-1 panchromatic (PAN), pan-sharpened (PS), and multispectral (MS) products were analyzed together with controlled simulations of system MTF, altitude-dependent GSD variation, and super-resolution processing. Among the native products, PS imagery showed the highest NIIRS and overall detection performance. In the controlled experiments, higher system MTF increased RER and NIIRS, while lower simulated altitude generally produced finer GSD and higher NIIRS for both PS and PAN products. However, detection performance varied by scene, product type, and target class and did not increase in direct proportion to NIIRS. In the super-resolution case study, ×2 SR provided the most consistent NIIRS improvement, whereas detection responses at higher SR scales were target class dependent. These results suggest that spatial image quality should be evaluated not only through interpretability metrics such as NIIRS but also in relation to practical downstream performance. The proposed framework provides a baseline for future constellation-scale image quality assessment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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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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        Value: 10.3390/rs18121943
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        Text: English
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        PageCount: 32
        StartPage: 1943
    Subjects:
      – SubjectFull: Image quality analysis
        Type: general
      – SubjectFull: Nanosatellites
        Type: general
      – SubjectFull: Image processing
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      – SubjectFull: Remote-sensing images
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      – SubjectFull: Optical engineering
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      – SubjectFull: Object recognition (Computer vision)
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      – SubjectFull: Spatial resolution
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              Text: Jun2026
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