Change Detection in Surface Features on Mars

Ongoing Mars exploration missions are returning enormous volumes of image data. Identifying surface changes in these images, e.g., new impact craters, is critical for investigating many scientific hypotheses. Traditional approaches to change detection rely on image differencing and manual feature engineering. These methods can be sensitive to irrelevant variations in illumination or image quality and typically require before and after images to be co-registered, which itself is a major challenge. To overcome these limitations, we are developing novel deep learning approaches to change detection that rely on transfer learning, convolutional autoencoders, and Siamese networks. Our experiments show that these approaches can detect meaningful changes with high accuracy despite significant differences in illumination, image quality, imaging sensors, and alignment between before and after images. Co-investigators: Dr. Kiri Wagstaff (JPL) and Dr. Brian Bue (JPL). This research was performed in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration and funded through the Internal Strategic University Research Partnerships (SURP) program. Research paper in preparation.