Farmland parcel-based crop classification in cloudy/rainy mountains using Sentinel-1 and Sentinel-2 based deep learning-Reference-Cited by-同舟云学术

Farmland parcel-based crop classification in cloudy/rainy mountains using Sentinel-1 and Sentinel-2 based deep learning

Published:2022-02-01 Issue:3 Volume:43 Page:1054-1073
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sun Yingwei¹,Li Zhao-Liang¹,Luo Jiancheng²³,Wu Tianjun⁴,Liu Niantang¹

Affiliation:

1. Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning (CAAS), Chinese Academy of Agricultural Sciences, Beijing, China

2. State Key Laboratory of Remote Sensing Science of the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

3. School of Science, University of Chinese Academy of Sciences, Beijing, China

4. School of Science, Chang’an University, Xi’an, China

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2032458

Reference46 articles.

1. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine

2. The integrated use of optical and InSAR data for urban land‐cover mapping

3. Synthesis of Multispectral Optical Images From SAR/Optical Multitemporal Data Using Conditional Generative Adversarial Networks

4. Improving Spring Maize Yield Estimation at Field Scale by Assimilating Time-Series HJ-1 CCD Data into the WOFOST Model Using a New Method with Fast Algorithms

5. Agricultural crop-type classification of multi-polarization SAR images using a hybrid entropy decomposition and support vector machine technique

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