Detecting and Refining HiRISE Image Patches Obscured by Atmospheric Dust
This addresses the issue of manual effort and lost flight time in Mars imaging for space agencies, but is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of atmospheric dust obscuring HiRISE images of Mars by automatically filtering out obstructed images with a Dust Image Classifier achieving 94.05% accuracy, and denoising partially obstructed images using an Auto Encoder and Pix2Pix GAN with SSIM indices of 0.75 and 0.99 respectively.
HiRISE (High-Resolution Imaging Science Experiment) is a camera onboard the Mars Reconnaissance orbiter responsible for photographing vast areas of the Martian surface in unprecedented detail. It can capture millions of incredible closeup images in minutes. However, Mars suffers from frequent regional and local dust storms hampering this data-collection process, and pipeline, resulting in loss of effort and crucial flight time. Removing these images manually requires a large amount of manpower. I filter out these images obstructed by atmospheric dust automatically by using a Dust Image Classifier fine-tuned on Resnet-50 with an accuracy of 94.05%. To further facilitate the seamless filtering of Images I design a prediction pipeline that classifies and stores these dusty patches. I also denoise partially obstructed images using an Auto Encoder-based denoiser and Pix2Pix GAN with 0.75 and 0.99 SSIM Index respectively.