Toward Generalized Change Detection on Planetary Surfaces with Convolutional Autoencoders and Transfer Learning

Journal Article (Journal Article)

Ongoing planetary exploration missions are returning large 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 coregistered, which itself is a major challenge. Additionally, most prior change detection studies have been limited to remote sensing images of earth. We propose a new deep learning approach for binary patch-level change detection involving transfer learning and nonlinear dimensionality reduction using convolutional autoencoders. Our experiments on diverse remote sensing datasets of Mars, the moon, and earth show that our methods can detect meaningful changes with high accuracy using a relatively small training dataset despite significant differences in illumination, image quality, imaging sensors, coregistration, and surface properties. We show that the latent representations learned by a convolutional autoencoder yield the most general representations for detecting change across surface feature types, scales, sensors, and planetary bodies.

Full Text

Duke Authors

Cited Authors

  • Kerner, HR; Wagstaff, KL; Bue, BD; Gray, PC; Iii, JFB; Amor, HB

Published Date

  • October 1, 2019

Published In

Volume / Issue

  • 12 / 10

Start / End Page

  • 3900 - 3918

Electronic International Standard Serial Number (EISSN)

  • 2151-1535

International Standard Serial Number (ISSN)

  • 1939-1404

Digital Object Identifier (DOI)

  • 10.1109/JSTARS.2019.2936771

Citation Source

  • Scopus