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COMPARATIVE ANALYSIS OF SATELLITE IMAGES STITCHING METHODS BASED ON LOCAL FEATURE DETECTION.

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Title: COMPARATIVE ANALYSIS OF SATELLITE IMAGES STITCHING METHODS BASED ON LOCAL FEATURE DETECTION.
Alternate Title: Порівняльний аналіз методів зшивання супутникових зображень на основі виявлення локальних ознак
Authors: Riabko, A. V.1 2383870@stud.kai.edu.ua, Hrishnenko, V. Y.1 1744220@stud.kai.edu.ua
Source: Electronics & Control Systems. 2025, Vol. 86 Issue 4, p86-92. 7p.
Subjects: Remote sensing, Descriptor systems, Remote-sensing images, Feature extraction
Abstract: This paper investigates feature-based methods for satellite image stitching under a unified evaluation framework. Four algorithms - SIFT, SURF, ORB and BRISK - are examined with respect to keypoint detection, descriptor formation, correspondence generation and geometric alignment. A standardized MATLAB workflow is employed: grayscale detection and description, nearest-neighbour matching with a ratio test, robust outlier rejection via RANSAC with model escalation and mask-based blending with content cropping. Approximately fifty image sets spanning diverse landforms are processed; a Sahara Desert example illustrates the protocol. The study's aim is to characterize the accuracy-efficiency trade-offs of vector (SIFT, SURF) and binary (ORB, BRISK) descriptors in realistic orbital conditions and to provide a transparent basis for method selection in remote-sensing workflows. [ABSTRACT FROM AUTHOR]
Copyright of Electronics & Control Systems is the property of National Aviation University 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: COMPARATIVE ANALYSIS OF SATELLITE IMAGES STITCHING METHODS BASED ON LOCAL FEATURE DETECTION.
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  Data: Порівняльний аналіз методів зшивання супутникових зображень на основі виявлення локальних ознак
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  Data: <searchLink fieldCode="JN" term="%22Electronics+%26+Control+Systems%22">Electronics & Control Systems</searchLink>. 2025, Vol. 86 Issue 4, p86-92. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptor+systems%22">Descriptor systems</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
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  Data: This paper investigates feature-based methods for satellite image stitching under a unified evaluation framework. Four algorithms - SIFT, SURF, ORB and BRISK - are examined with respect to keypoint detection, descriptor formation, correspondence generation and geometric alignment. A standardized MATLAB workflow is employed: grayscale detection and description, nearest-neighbour matching with a ratio test, robust outlier rejection via RANSAC with model escalation and mask-based blending with content cropping. Approximately fifty image sets spanning diverse landforms are processed; a Sahara Desert example illustrates the protocol. The study's aim is to characterize the accuracy-efficiency trade-offs of vector (SIFT, SURF) and binary (ORB, BRISK) descriptors in realistic orbital conditions and to provide a transparent basis for method selection in remote-sensing workflows. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Electronics & Control Systems is the property of National Aviation University 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: 10.18372/1990-5548.86.20627
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        Text: English
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      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Descriptor systems
        Type: general
      – SubjectFull: Remote-sensing images
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      – SubjectFull: Feature extraction
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              Text: 2025
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