Automated Detection and Classification of Lunar Linear Tectonic Features Using a Deep Learning Method

On the lunar surface, wrinkle ridges, grabens, and lobate scarps represent key tectonic landforms that reflect the evolution of the Moon’s stress field and its tectonic processes. However, these linear structures often exhibit weak textures, low contrast, and large scale variations, making manual interpretation inefficient and subjective. To address this issue, this study introduces an improved YOLOv8 model, termed HL-YOLOv8, for the automated detection of lunar linear features. The model incorporates a multiscale lightweight channel attention (C2f_MLCA) module into the backbone network to enhance the extraction of fine-grained and weak-texture features and integrates a multihead self-attention (C2f_MHSA) module in the feature fusion stage to improve the modelling of long-range spatial dependencies. In addition, the combination of a dual focal loss and a diversified data augmentation strategy effectively mitigates the detection difficulties caused by class imbalance and weak-feature samples. The experimental results obtained using the global LROC-WAC image dataset demonstrate that HL-YOLOv8 significantly outperforms the baseline YOLOv8 and other comparative models in terms of precision, recall, and [email protected]. Specifically, the proposed model achieved an average precision of 73.5%, an average recall of 73.1%, and an average [email protected] of 74.6% on the evaluation dataset, showing particularly strong performance in detecting elongated grabens and boundary-blurred lobate scarps. The global distribution maps derived from the model predictions indicate that HL-YOLOv8 can be applied to comprehensively reconstruct the spatial patterns of the three types of linear structures and identify potential new features in high-latitude and geologically complex regions, demonstrating excellent generalizability and robustness. This study provides an efficient and reliable framework for the automated identification and global mapping of lunar linear features and offers a transferable methodological reference for the tectonic interpretation of terrestrial planets.

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Publication Details

Journal
Remote Sensing
Published
2026-04-26
DOI
https://doi.org/10.3390/rs18091330
Primary Topic
Planetary Science and Exploration
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated Detection and Classification of Lunar Linear Tectonic Features Using a Deep Learning Method

Jianguo Yan, Yaowen Luo, Wensong Zhang, Jianhui Wang et al.
Remote Sensing
Planetary Science and Exploration
article

Automated Detection and Classification of Lunar Linear Tectonic Features Using a Deep Learning Method

Jianguo Yan, Yaowen Luo, Wensong Zhang, Jianhui Wang, Yang Luo, Xiaoyang Liu, Denggao Qiu
article en

Abstract

On the lunar surface, wrinkle ridges, grabens, and lobate scarps represent key tectonic landforms that reflect the evolution of the Moon’s stress field and its tectonic processes. However, these linear structures often exhibit weak textures, low contrast, and large scale variations, making manual interpretation inefficient and subjective. To address this issue, this study introduces an improved YOLOv8 model, termed HL-YOLOv8, for the automated detection of lunar linear features. The model incorporates a multiscale lightweight channel attention (C2f_MLCA) module into the backbone network to enhance the extraction of fine-grained and weak-texture features and integrates a multihead self-attention (C2f_MHSA) module in the feature fusion stage to improve the modelling of long-range spatial dependencies. In addition, the combination of a dual focal loss and a diversified data augmentation strategy effectively mitigates the detection difficulties caused by class imbalance and weak-feature samples. The experimental results obtained using the global LROC-WAC image dataset demonstrate that HL-YOLOv8 significantly outperforms the baseline YOLOv8 and other comparative models in terms of precision, recall, and [email protected]. Specifically, the proposed model achieved an average precision of 73.5%, an average recall of 73.1%, and an average [email protected] of 74.6% on the evaluation dataset, showing particularly strong performance in detecting elongated grabens and boundary-blurred lobate scarps. The global distribution maps derived from the model predictions indicate that HL-YOLOv8 can be applied to comprehensively reconstruct the spatial patterns of the three types of linear structures and identify potential new features in high-latitude and geologically complex regions, demonstrating excellent generalizability and robustness. This study provides an efficient and reliable framework for the automated identification and global mapping of lunar linear features and offers a transferable methodological reference for the tectonic interpretation of terrestrial planets.

Remote SensingVol. 18(9)
Chinese Academy of Sciences (CN), Xinjiang Astronomical Observatory (CN), Wuhan University (CN), Henan Academy of Sciences (CN), Institute of Disaster Prevention (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), Geological Exploration Technology Institute of Jiangsu Province (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing
Life below water
Openalex Percentile: Top 6%
Planetary Science and Exploration
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