Crop classification based on G-CNN using multi-scale remote sensing images-Reference-Cited by-同舟云学术

Crop classification based on G-CNN using multi-scale remote sensing images

Published:2024-08-22 Issue:9 Volume:15 Page:941-950
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Meng Mengmeng¹²,Zhang Kaixin¹³⁴,Huang Yabo¹³⁴,Li Ning¹³⁴^ORCID,Guo Zhengwei¹³⁴,Zhou Zhimin⁵

Affiliation:

1. School of Computer and Information Engineering, Henan University, Kaifeng, China

2. School of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu, China

3. Henan Province Engineering Research Center of Spatial Information Processing, Henan University, Kaifeng, China

4. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China

5. College of Geography and Environmental Science, Henan University, Kaifeng, China

Funder

National Natural Science Foundation of China

the College Key Research Project of Henan Province

the Plan of Science and Technology of Henan Province

the Key Laboratory of Natural Resources Monitoring and Regulation in Southern Hilly Region, Ministry of Natural Resources of the People’s Republic of China

Plan of Science and Technology of Department of Natural Resources of Hunan Province

Ministry of Natural Resources

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2024.2388848

Reference12 articles.

1. Crop type classification with combined spectral, texture, and radar features of time-series Sentinel-1 and Sentinel-2 data

2. In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

3. In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series

4. Classification and status monitoring of agricultural crops in central Morocco: a synergistic combination of OBIA approach and fused Landsat-Sentinel-2 data

5. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery