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

Hannah Rae Kerner, Kiri L. Wagstaff, Brian D. Bue, Patrick C. Gray, James F.Bell Iii, Heni Ben Amor

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8827273
Pages (from-to)3900-3918
Number of pages19
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume12
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

  • Change detection algorithms
  • earth
  • machine learning
  • mars
  • moon
  • neural networks
  • remote sensing
  • supervised learning
  • unsupervised learning

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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