Thesis (M.S.)--University of Wisconsin--Madison. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 24-25).

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): Kenneth L. Davidson, Andreas K. Goroch. Includes bibliographical references (p. 47-48). Also available online.

Radio frequency interference (RFI) poses significant challenges for passive microwave radiometry used in climate studies and Earth science. Despite operating in protected frequency bands, microwave radiometers often encounter RFI from sources like air surveillance radars, 5G communications, and unmanned aerial vehicles. Traditional RFI detection methods rely on handcrafted algorithms designed for specific RFI types. This study proposes a deep learning (DL) approach, leveraging convolutional neural networks to detect various RFI types on a global scale. By learning directly from radiometer data, this data-driven method enhances detection accuracy and generalization. The DL framework processes raw moment data and Stokes parameters, dynamically labeled using quality flags, offering a robust and efficient solution for RFI detection. This approach demonstrates the potential for improved RFI mitigation in passive remote sensing applications.