Wildfire Monitring Based on LSTM and Deep Learning-Reference-Cited by-同舟云学术

Wildfire Monitring Based on LSTM and Deep Learning

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

Author:

Zhang Haoyu¹,Zheng Zezhong²,Wen Gang³

Affiliation:

1. Glasgow College, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731

2. School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu,Sichuan,PRC,611731

3. Electric Power Research Institute, Yunnan Power Grid Company Ltd., China Southern Power Grid,Joint Laboratory of power remote sensing technology,Kunming,Yunnan,PRC,650217

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09883872.pdf?arnumber=9883872

Reference9 articles.

1. A review of AVHRR-based active fire detection algorithms: Principles, limitations, and recommendations;li;Global and Regional Vegetation Fire Monitoring From Space Planning and Coordinated International Effort,2001

2. Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

3. The Reprocessing for Himawari-8 Based on Deep Learning

4. Forest fire detection based on MODIS satellite imagery, and comparison of NOAA satellite imagery with fire fighters'Information;nakau;IARC/JAXA Terrestrial Team Workshop,2006

5. The collection 6 MODIS active fire detection algorithm and fire products

Cited by 1 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. Lsdnet: A Lightweight Segmentation Model for Real-Time Detection of Wildfires;2023