Hyperspectral Imaging Methods Improve RGB Image Semantic Segmentation of Unstructured Terrains-Reference-Cited by-同舟云学术

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Hyperspectral Imaging Methods Improve RGB Image Semantic Segmentation of Unstructured Terrains

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:2020 International Conference Mechatronic Systems and Materials (MSM)
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Author:

Liyanage Dhanushka C.,Hudjakov Robert,Tamre Mart

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9199168/9201613/09201738.pdf?arnumber=9201738

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Image Fusion Techniques for Off-Road Semantic Segmentation in Harsh Lighting Conditions. A Multispectral Imagery Analysis;2024 21st International Conference on Ubiquitous Robots (UR);2024-06-24

2. Road condition estimation using deep learning with hyperspectral images: detection of water and snow;Proceedings of the Estonian Academy of Sciences;2024

3. Real-Time Semantic Segmentation Using Hyperspectral Images for Unstructured and Unknown Environments;2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS);2023-10-31

4. Semantic Segmentation Using U-Net Deep Learning Network for Quince Phenotyping on RGB and HyperSpectral Images;2023 27th International Conference Electronics;2023-06-19

5. Hyperspectral Imaging for Vehicle Traction Effort Prediction: ISEAUTO case study;2023 IEEE 17th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG);2023-06-14

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