文献类型： 外文期刊

作者： Zhu, Shuang ¹ ; Zhang, Jinshui ² ; Shuai, Guanyuan ⁴ ; Liu Hongli ² ; Feng Zhang ² ; Zheng Dong ⁵ ;

作者机构： 1.Beijing Polytech Coll, Beijing 100042, Peoples R China

2.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Michigan State Univ, Dept Earth & Environm Sci, E Lansing, MI 48824 USA

5.Beijing Vocat Transportat Coll, Beijing 102618, Peoples R China

关键词： Transfer learning; Convolutional Neural Network; RSNet; Crop classification; Deep learning

期刊名称：IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM

ISSN： 2153-6996

年卷期： 2020 年

页码：

收录情况： SCI

摘要： Deep learning (DL) method has been a state-of-art method for land cover mapping. However, DL is so data-hungry which makes it difficult to obtain the large amount label data feed into the process to ensure high performance. In this paper, we utilized the historical data which is the crop distribution map extracted by support vector machine algorithm as training dataset which was produced by traditional classification and was used to train a convolution neural network as a basic model, called as RSNet. Then, the basic model was fine-tuned which is usually used in this deep learning model training to improve the classification accuracy. The F1 scores of rice and maize were 78.91% and 72.94%, respectively. The result verified that the migration ability of RSNet model is strong to produce the current year crop map using the historical labeled samples. From the fine-tuning model, higher overall classification accuracy of crop classification was achieved as 90.36%. This classification strategy brings us a bright future to map the crop distribution supported by the historical labelled dataset, which solves the data-hungry issues.