Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning-Reference-Cited by-同舟云学术

Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning

Published:2023 Issue: Volume:16 Page:3060-3069
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:

Shen Qianhui¹^ORCID,Deng Haojun¹^ORCID,Wen Xinjian²^ORCID,Chen Zhanpeng²,Xu Hongfei²

Affiliation:

1. Guangdong Provincial Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

2. Surveying and Mapping Institute Lands and Resource, Department of Guangdong Province, Guangzhou, China

Funder

National Key R&D Program of China

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/ielx7/4609443/9973430/10065448.pdf?arnumber=10065448

Reference50 articles.

1. Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud