Dense Greenhouse Extraction in High Spatial Resolution Remote Sensing Imagery-Reference-Cited by-同舟云学术

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Dense Greenhouse Extraction in High Spatial Resolution Remote Sensing Imagery

Published:2020-09-26 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Chen Dingyuan,Zhong Yanfei,Ma Ailong,Cao Liqin

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9323073/9323063/09323371.pdf?arnumber=9323371

Cited by 7 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. Remote sensing detection and mapping of plastic greenhouses based on YOLOX+: A case study in Weifang, China;Computers and Electronics in Agriculture;2024-03

2. Effects of Atmospheric Correction and Image Enhancement on Effective Plastic Greenhouse Segments Based on a Semi-Automatic Extraction Method;ISPRS International Journal of Geo-Information;2022-11-23

3. EAGNet: A method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention;Computers and Electronics in Agriculture;2022-11

4. PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning;Remote Sensing;2022-10-10

5. Plastic Greenhouses Detection from Alsat-2A Satellite Data Using Mask R-CNN;2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS);2022-03-07

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