Crater-DETR: A Novel Transformer Network for Crater Detection Based on Dense Supervision and Multiscale Fusion-Reference-Cited by-同舟云学术

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Crater-DETR: A Novel Transformer Network for Crater Detection Based on Dense Supervision and Multiscale Fusion

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Guo Yue¹^ORCID,Wu Hao¹^ORCID,Yang Shuojin¹^ORCID,Cai Zhanchuan¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China

Funder

Science and Technology Development Fund of Macau

Zhuhai Industry-University-Research Collaboration Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10466735.pdf?arnumber=10466735

Reference52 articles.

1. A Novel Approach for Multiscale Lunar Crater Detection by the Use of Path-Profile and Isolation Forest Based on High-Resolution Planetary Images

2. AE-TransUNet+: An Enhanced Hybrid Transformer Network for Detection of Lunar South Small Craters in LRO NAC Images

3. High-Resolution Feature Pyramid Network for Automatic Crater Detection on Mars

4. Crater Detection Using Unsupervised Algorithms and Convolutional Neural Networks

5. Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters

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