Using Quantum ML and Analyzing Electro-Optical vs SAR Image Classification Through Polarizati
By Naisha Patel
Intelligence agencies aim to find the most efficient methods to combat adversaries. The efficiency of image processing from satellite images is essential to ensure information about the world is being recorded. Two types of images, SAR and Electro-Optical Images, have different purposes, advantages, and disadvantages. SAR Images are reliable during any time of day across any weather type. Electro-Optical Images are dependent on the sun's presence, however, create more detailed images. Both types of images may not be valued the same amount depending on how well they can classify certain images in certain areas. Using Quantum ML to create my classification model, and using a dataset of civilian vehicles that have been modified to account for polarizations, noise, and terrain, I aim to investigate which type of image is more beneficial for intelligence agencies.