Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images-Reference-Cited by-同舟云学术

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Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images

Published:2019-12-26 Issue:8 Volume:41 Page:3162-3174
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Ji Shunping¹,Zhang Zhili¹,Zhang Chi¹,Wei Shiqing¹,Lu Meng²,Duan Yulin³^ORCID

Affiliation:

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

2. Faculty of Geoscience, Department of Physical Geography, Utrecht University, Utrecht, The Netherlands

3. Chinese Academy of Agricultural Sciences, Institute of Agricultural Resources and Regional Planning, Beijing, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1699973

Reference29 articles.

1. IEEE Transactions on Pattern Analysis and Machine Intelligence

2. Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle

3. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

4. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

5. RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY

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