Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images-Reference-Cited by-同舟云学术

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Application of an Improved U-Net Neural Network on Fracture Segmentation from Outcrop Images

Published:2022-07-17 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Wang Zhibao¹,Zhang Ziming¹,Bai Lu²,Yang Yuze¹,Ma Qiang³

Affiliation:

1. School of Computer and Information Technology, Northeast Petroleum University,Daqing,China

2. School of Computing, Ulster University,Belfast,UK

3. College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University,Daqing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09883208.pdf?arnumber=9883208

Reference18 articles.

1. Detection of oil wells based on faster R-CNN in optical satellite remote sensing images

2. Deforestation Detection Based on U-Net and LSTM in Optical Satellite Remote Sensing Images

3. Deep Learning Application for Fracture Segmentation Over Outcrop Images from UAV-Based Digital Photogrammetry

4. Fully convolutional networks for semantic segmentation

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2. A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning;Sensors;2024-02-26

3. Optimized faster R-CNN for oil wells detection from high-resolution remote sensing images;International Journal of Remote Sensing;2023-11-13

4. Detection of over-ground petroleum and gas pipelines from optical remote sensing images;Image and Signal Processing for Remote Sensing XXIX;2023-10-19

5. Spatial information extraction of oil well sites based on medium-resolution satellite imagery;Image and Signal Processing for Remote Sensing XXIX;2023-10-19

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