Clusterformer for Pine Tree Disease Identification Based on UAV Remote Sensing Image Segmentation-Reference-Cited by-同舟云学术

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Clusterformer for Pine Tree Disease Identification Based on UAV Remote Sensing Image Segmentation

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Huan¹^ORCID,Li Wei¹^ORCID,Jia Wen²^ORCID,Sun Hong³,Zhang Mengmeng¹^ORCID,Song Lujie¹,Gui Yuanyuan¹

Affiliation:

1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

2. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China

3. Center for Biological Disaster Prevention and Control, National Forestry and Grassland Administration, Shenyang, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10422983.pdf?arnumber=10422983

Reference62 articles.

1. Preliminary study on larix spp. infected by bursaphelenchus xylophilus in natural environment;Yu;For. Pest Dis,2019

2. Pine Wilt Disease: a threat to European forestry

3. The First Record of Monochamus saltuarius (Coleoptera; Cerambycidae) as Vector of Bursaphelenchus xylophilus and Its New Potential Hosts in China

4. Inference of the worldwide invasion routes of the pinewood nematode Bursaphelenchus xylophilus using approximate Bayesian computation analysis

5. Hyperspectral Analysis of Pine Wilt Disease to Determine an Optimal Detection Index

Cited by 2 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. Self-Supervised Learning With Multiscale Densely Connected Network for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

2. Attention Network With Fractional-Ordered Mixed Geometric Features for Hyperspectral Image Classification;IEEE Transactions on Geoscience and Remote Sensing;2024

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