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

文献类型： 外文期刊

第一作者： Ji, Shunping

作者： Ji, Shunping;Zhang, Chi;Xu, Anjian;Xu, Anjian;Shi, Yun;Duan, Yulin

作者机构：

关键词： 3D convolution;convolutional neural networks;crop classification;multi-temporal remote sensing images;active learning

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2018 年 10 卷 1 期

页码：

收录情况： SCI

摘要： 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.

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