Leveraging Multisource Label Learning for Underground Object Recognition-Reference-Cited by-同舟云学术

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Leveraging Multisource Label Learning for Underground Object Recognition

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Lyu Derui¹^ORCID,Chen Lyuzhou¹^ORCID,Ban Taiyu¹^ORCID,Wang Xiangyu¹^ORCID,Zhu Qinrui²^ORCID,Zhou Xiren¹^ORCID,Chen Huanhuan¹^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

2. School of Computer Science and Technology and the Laboratory for Big Data and Decision, University of Science and Technology of China, Hefei, China

Funder

National Key Research and Development Program of China

National Nature Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10648621.pdf?arnumber=10648621

Reference51 articles.

1. Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks

2. Buried utility pipeline mapping based on multiple spatial data sources: A Bayesian data fusion approach;Chen

3. An Automated GPR Signal Denoising Scheme Based on Mode Decomposition and Principal Component Analysis

4. Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data

5. Simple and Efficient: A Semisupervised Learning Framework for Remote Sensing Image Semantic Segmentation

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