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    This study addresses nuisance signals in Earth-viewing space imaging by using tomographic reconstructions. It explores methods to improve altitude-axis resolution, a key challenge for this technique.

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    Area of Science:

    • Earth observation science
    • Remote sensing technology
    • Atmospheric physics

    Background:

    • Space-based imaging instruments face interference from signals like cloud reflections, ground reflections, and OH-airglow emissions.
    • These nuisance signals hinder precise Earth observation tasks.
    • Current signal separation methods require refinement for better resolution.

    Purpose of the Study:

    • To investigate methods for separating nuisance signals in Earth-viewing space imaging.
    • To address the challenge of altitude-axis resolution in tomographic reconstructions.
    • To analyze the implementation of the maximum likelihood expectation maximization algorithm for signal separation.

    Main Methods:

    • Utilizing tomographic reconstructions to differentiate between desired signals and interference.
    • Exploring various techniques to enhance altitude-axis resolution in imaging data.
    • Implementing and evaluating the maximum likelihood expectation maximization (MLEM) algorithm.

    Main Results:

    • Demonstrated the effectiveness of tomographic reconstruction in separating nuisance signals.
    • Identified and discussed methods to improve altitude-axis resolution.
    • Provided an analysis of the MLEM algorithm's performance in this context.

    Conclusions:

    • Tomographic reconstruction is a viable method for mitigating nuisance signals in Earth-viewing space imaging.
    • Improving altitude-axis resolution remains a critical area for advancement.
    • The MLEM algorithm shows promise for enhancing signal separation accuracy.