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LIU Zipeng, CAI Jinhao, HU Teng, et al. Extraction of lunar wrinkle ridges structure based on multimodal semantic segmentation[J]. Journal of Radars, in press. doi: 10.12000/JR25279
Citation: LIU Zipeng, CAI Jinhao, HU Teng, et al. Extraction of lunar wrinkle ridges structure based on multimodal semantic segmentation[J]. Journal of Radars, in press. doi: 10.12000/JR25279
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Extraction of Lunar Wrinkle Ridges Structure Based on Multimodal Semantic Segmentation

DOI: 10.12000/JR25279 CSTR: 32380.14.JR25279
  • 1.

    School of Science, China University of Geosciences Beijing, Beijing 100083, China

  • 2.

    School of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, China

  • 3.

    Lunar and Planetary Remote Sensing Exploration Research Center, China University of Geosciences Beijing, Beijing 100083, China

  • 4.

    Hebei Key Laboratory of Aerospace Information and Intelligentized Surveying and Mapping, China University of Geosciences Beijing, Beijing 100083, China

  • 5.

    International Cooperation Research Center for Lunar and Planetary Exploration, Joint Research Center for Deep Space Exploration, Ministry of Education, Beijing 100083, China

Funds:  The National Natural Science Foundation of China (62495033)
More Information
  • Corresponding author: KANG Zhizhong, zzkang@cugb.edu.cn
  • Received Date: 2025-12-30
    Available Online: 2026-03-25
    Abstract
  • Lunar wrinkle ridges are important linear structures that are widely distributed in the mare regions on the lunar surface and are of great importance for studying the evolution of the lunar stress field and the history of volcanic activities. Traditional lunar wrinkle ridge recognition and cataloging mainly rely on manual interpretation, which is inefficient and subjective. In this paper, an automatic lunar wrinkle ridge extraction method based on multimodal semantic segmentation is proposed. By constructing a high-quality lunar wrinkle ridge remote sensing image annotation dataset and introducing synthetic aperture radar data, a DeepLabv3+-based multimodal semantic segmentation network (WR-Net) is constructed through iterative training. A dynamic fusion module and an attention mechanism were introduced into WR-Net, which effectively optimized the feature extraction and fusion process of multimodal images and markedly improved the robustness and accuracy of the model. On the multimodal lunar wrinkle ridge test set, WR-Net achieved excellent performance (Precision = 95.516%, Recall = 89.963%, F1-Score = 92.657%, and MIoU = 92.944%). Furthermore, we used WR-Net to automatically identify and extract the lunar wrinkle ridges from the 70° south latitude to the 70° north latitude of the moon and cataloged and statistically analyzed the results. The proposed method is suitable for the recognition of lunar wrinkle ridges and provides an effective paradigm for the automatic extraction of similar linear structures on the moon and other planetary bodies.

     

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