Abstract
Change detection (CD) is critical for analyzing data collected by planetary exploration missions, e.g., for identification of new impact craters. However, CD is still a relatively new topic in the context of planetary exploration. Sheer variation of planetary data makes CD much more challenging than in the case of Earth observation (EO). Unlike CD for EO, patch-level decision is preferred in planetary exploration as it is difficult to obtain perfect pixelwise alignment/coregistration between the bi-temporal planetary images. Lack of labeled bi-temporal data impedes supervised CD. To overcome these challenges, we propose an unsupervised CD method that exploits a pretrained feature extractor to obtain bi-temporal deep features that are further processed using global max-pooling to obtain patch-level feature description. Bi-temporal patch-level features are further analyzed based on difference to determine whether a patch is changed. Additionally, a self-supervised method is proposed to estimate the decision boundary between the changed and unchanged patches. Experimental results on three planetary CD datasets from two different planetary bodies (Mars and Moon) demonstrate that the proposed method often outperforms supervised planetary CD methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.lrz.de/ai4eo/cd/-/tree/main/planetaryCDUnsup</uri> .
Highlights
I NTEREST in planetary exploration missions has increased significantly in the last decade [1] as such missions enrich our knowledge about the solar system [2]
A crucial role in most such missions is played by the scientific imaging instruments that are used for various purposes, including planetary surface characterization and spectral mapping for mineralogy
Deep transfer learning methods that exploit a pretrained model for bi-temporal feature extraction and comparison have shown excellent performance in different Change detection (CD) applications [5], [6]. Inspired by this we propose a deep transfer learning based CD method that ingests bi-temporal patches and processes it through a set of convolution layers
Summary
I NTEREST in planetary exploration missions has increased significantly in the last decade [1] as such missions enrich our knowledge about the solar system [2]. CD plays crucial role in several EO tasks, e.g., disaster management [5], urban monitoring [6], and military applications. Just like EO, CD may play a significant role in planetary explorations. As detailed by Kerner et al [3], one such application of CD in planetary exploration is to monitor the changes induced by meteorite impact. Such impacts strongly alter the landscape of the planets. Another such application is monitoring of recurring slope lineae (RSL) that appear/disappear on surface of Mars on timescales close to a year
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