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Enhanced Multispectral Band-to-Band Registration Using Co-Occurrence Scale Space and Spatial Confined RANSAC Guided

Indranil Misra, Mukesh Kumar Rohil, S Manthira Moorthi

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    Summary
    This summary is machine-generated.

    This study introduces a new method for Band-to-Band Registration (BBR) in remote sensing, improving spectral channel alignment. The novel technique enhances image accuracy, outperforming existing methods for multispectral satellite data.

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    Area of Science:

    • Remote Sensing
    • Image Processing
    • Geospatial Analysis

    Background:

    • Band-to-Band Registration (BBR) is critical for multispectral remote sensing sensors.
    • Accurate alignment of spectral channels at sub-pixel level is essential for reliable data analysis.
    • Existing BBR methods may face challenges with complex landscapes and cloud cover.

    Purpose of the Study:

    • To develop and evaluate a novel BBR technique for high-accuracy spectral channel alignment.
    • To assess the impact of band misregistration on vegetation index calculations.
    • To demonstrate the superiority of the proposed BBR method over state-of-the-art techniques.

    Main Methods:

    • Utilized Co-occurrence Scale Space (CSS) for robust feature point detection.
    • Employed Spatial Confined RANSAC (SC-RANSAC) to effectively remove outlier control points.
    • Implemented a Segmented Affine Transformation (SAT) model to minimize distortion and ensure registration consistency.

    Main Results:

    • The novel BBR technique demonstrated superior performance in aligning spectral channels.
    • Analysis showed significant impact of band misregistration on Normalized Difference Vegetation Index (NDVI).
    • The proposed method outperformed existing BBR techniques in experimental evaluations.

    Conclusions:

    • The developed BBR method provides accurate spectral channel alignment for multispectral imagery.
    • The technique is validated on diverse landscapes using Indian Nano Satellite (INS-2B) data.
    • The proposed approach offers a significant advancement in remote sensing image processing.