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A Machine Learning-Based Semantic Pattern Matching Model for Remote Sensing Data Registration

  • Research Article
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Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

A Research Article was published on 20 February 2023

Abstract

Remote sensing image registration can benefit from a machine learning method based on the likelihood of predicting semantic spatial position distributions. Semantic segmentation of images has been revolutionized due to the accessibility of high-resolution remote sensing images and the advancement of machine learning techniques. This system captures the semantic distribution location of the matching reference picture, which ML mapped using learning-based algorithms. The affine invariant is utilized to determine the semantic template’s barycenter position and the pixel’s center, which changes the semantic border alignment problem into a point-to-point matching issue for the machine learning-based semantic pattern matching (ML-SPM) model. The first step examines how various factors such as template radius, training label filling form, or loss function combination affect matching accuracy. In this second step, the matching of sub-images (MSI) images is compared using heatmaps created from the expected similarity between the images’ cropped sub-images. Images having radiometric discrepancies are matched with excellent accuracy by the approach. SAR-optical image matching has never been easier, and now even large-scale sceneries can be registered using this approach, which is a significant advance over previous methods. Optical satellite imaging or multi-sensor stereogrammetry can be combined with both forms of data to enhance geolocation.

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Funding

No funds, grants were received by any of the authors.

Author information

Authors and Affiliations

  1. Department of Computer Science, Dijlah University College, Baghdad, 10021, Iraq

    Mustafa Musa Jaber & Sura Khalil Abd

  2. Department of Computer Science, Al-Turath University College, Baghdad, Iraq

    Mustafa Musa Jaber

  3. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, 10023, Iraq

    Mohammed Hasan Ali

  4. Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, 10011, Iraq

    Mustafa Mohammed Jassim

  5. College of Technical Engineering, The Islamic University, Najaf, Iraq

    Ahmed Alkhayyat

  6. Medical Physics Department, Al-Mustaqbal University College, Babylon, Iraq

    Baraa A. Alreda

  7. Computer Technical Engineering, Mazaya University College, Nasiriyah, Thi Qar, Iraq

    Ahmed Rashid Alkhuwaylidee

  8. Department of Medical Instrumentation Engineering Techniques, Dijlah University College, Baghdad, Iraq

    Shahad Alyousif

  9. Department of Electrical and Electronic Engineering, College of Engineering, Gulf University, Almasnad, Kingdom of Bahrain

    Shahad Alyousif

Authors
  1. Mustafa Musa Jaber
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  2. Mohammed Hasan Ali
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  3. Sura Khalil Abd
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  4. Mustafa Mohammed Jassim
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  5. Ahmed Alkhayyat
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  6. Baraa A. Alreda
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  7. Ahmed Rashid Alkhuwaylidee
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  8. Shahad Alyousif
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Contributions

All Author are contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

Corresponding author

Correspondence to Mustafa Musa Jaber.

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Jaber, M.M., Ali, M.H., Abd, S.K. et al. A Machine Learning-Based Semantic Pattern Matching Model for Remote Sensing Data Registration. J Indian Soc Remote Sens 50, 2303–2316 (2022). https://doi.org/10.1007/s12524-022-01604-w

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  • DOI: https://doi.org/10.1007/s12524-022-01604-w

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