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Automated Lunar Crater Identification with Chandrayaan-2 TMC-2 Images using Deep Convolutional Neural Networks.

Mimansa Sinha1, Sanchita Paul1, Mili Ghosh2

  • 1Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India.

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Summary

This study introduces a deep learning model for identifying lunar craters using Chandrayaan-2 satellite images. The crater detection model achieves high accuracy, improving upon traditional methods for planetary geomorphology analysis.

Keywords:
Canny edge detectionChandrayaan-2Deep learningFCNNResNetTMC-2U-Net

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Area of Science:

  • Planetary Science
  • Geomorphology
  • Remote Sensing

Background:

  • Impact craters are key features on terrestrial planets and moons, influencing planetary geomorphology.
  • Existing crater identification methods face challenges due to variations in crater size, shape, and location.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate lunar crater detection.
  • To utilize images from the Terrain Mapping Camera-2 (TMC-2) onboard Chandrayaan-II for lunar crater identification.

Main Methods:

  • A U-Net convolutional neural network model with Resnet18 as a backbone was employed for image segmentation.
  • The model was initially trained using ImageNet weights and subsequently applied to TMC-2 ortho images from Chandrayaan-2.
  • The crater detection process involved a neural network, feature extraction, and an optimization technique.

Main Results:

  • The proposed model achieved 80.95% accuracy with unannotated data.
  • With annotated data, the model demonstrated improved precision and recall, reaching 86.91% accuracy in object detection.
  • The study utilized 2000 TMC-2 images, noting that more data could further enhance performance.

Conclusions:

  • The developed deep learning model offers a more accurate approach to lunar crater detection compared to traditional methods.
  • The U-Net based model shows significant potential for analyzing lunar surface features and contributing to planetary geomorphology studies.
  • Future work could involve incorporating a larger dataset to optimize the model's performance.