Object Detection in Large-Scale Remote Sensing Images With a Distributed Deep Learning Framework-Reference-Cited by-同舟云学术

Object Detection in Large-Scale Remote Sensing Images With a Distributed Deep Learning Framework

Published:2022 Issue: Volume:15 Page:8142-8154
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Liu Linkai¹^ORCID,Liu Yuanxing¹,Yan Jining¹^ORCID,Liu Hong¹,Li Mingming²,Wang Jinlin³,Zhou Kefa³

Affiliation:

1. School of Computer Science, China University of Geoscience, Wuhan, China

2. Soarscape Technology Development (Shanghai) Company, Ltd., Chinese Academy of Sciences, Urumqi, China

3. Xinjiang Key Laboratory of Mineral Resources and Digital Geology and Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09888013.pdf?arnumber=9888013

Reference61 articles.

1. Caffeonspark,2016

2. BigDL

3. Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

4. An Effective Data Fusion Model for Detecting the Risk of Transmission Line in Smart Grid

5. Hadoop based real-time Big Data Architecture for remote sensing Earth Observatory System

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