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A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space
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Integration model of POSP measurement spatial response function.

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    Accurate modeling requires accounting for the actual spatial response function (SRF) of the Particulate Observing Scanning Polarimeter (POSP). A new discrete integration method improves data fusion accuracy by considering actual sampling weights, reducing errors by 5-30%.

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    Area of Science:

    • Earth Observation
    • Atmospheric Science
    • Remote Sensing Technology

    Background:

    • The spatial response function (SRF) of the Particulate Observing Scanning Polarimeter (POSP) describes the weighted contribution of each location within its measurement footprint.
    • Accurate modeling necessitates an SRF that accounts for the dwell time of each location during the overall sampling integration time.
    • Using a mean value SRF, which assumes equal weighting, introduces errors, especially when fusing POSP data with high-resolution sensors.

    Purpose of the Study:

    • To propose a discrete integration method for calculating the POSP SRF that incorporates actual sampling weights.
    • To evaluate the accuracy of the proposed integration SRF model compared to a mean value SRF model.
    • To assess the impact of the integration SRF on data fusion with high spatial resolution sensors.

    Main Methods:

    • A discrete integration method was developed to calculate the SRF, considering the actual dwell time weights of each location within the instantaneous field of view (IFOV).
    • The proposed integral model was compared with a mean value model through simulations, analyzing the impact of intensity changes within the sampling area.
    • The integration SRF was validated using data from the Simultaneous Imaging Polarization Camera (SIPC) during an aerial experiment, comparing it with co-acquired POSP data.

    Main Results:

    • Simulations demonstrated that the difference between the integral and mean value models increases with greater intensity changes within the POSP's IFOV during a single sampling.
    • Validation using SIPC data confirmed that the integration SRF model more accurately characterizes POSP measurement details than the mean value SRF model.
    • The proposed SRF model reduced the root mean square error (RMSE) of convolved results and measurements by 5% to 30% across scenes with varying radiance contrast.

    Conclusions:

    • The discrete integration method accurately captures the POSP SRF by considering actual sampling weights, outperforming the mean value approach.
    • This improved SRF is crucial for accurate data fusion between POSP and high spatial resolution sensors.
    • The developed integration SRF model enhances the precision of remote sensing measurements and data analysis in Earth observation.