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Remote Sens. 2018, 10(1), 75; https://doi.org/10.3390/rs10010075

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

1
School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Xi’an Technique Center of Surveying and Mapping, Xi’an 710054, China
3
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
*
Author to whom correspondence should be addressed.
Received: 16 November 2017 / Revised: 3 January 2018 / Accepted: 4 January 2018 / Published: 7 January 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods. View Full-Text
Keywords: 3D convolution; convolutional neural networks; crop classification; multi-temporal remote sensing images; active learning 3D convolution; convolutional neural networks; crop classification; multi-temporal remote sensing images; active learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ji, S.; Zhang, C.; Xu, A.; Shi, Y.; Duan, Y. 3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images. Remote Sens. 2018, 10, 75.

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