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

文献类型： 外文期刊

第一作者： Sun, Yingwei

作者： Sun, Yingwei;Li, Zhao-Liang;Liu, Niantang;Luo, Jiancheng;Luo, Jiancheng;Wu, Tianjun

作者机构：

关键词： cloudy; rainy mountainous area; crop classification; multitemporal data; combined utilization; feature transfer; optimization

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.531； 五年影响因子：3.79 ）

ISSN： 0143-1161

年卷期： 2022 年 43 卷 3 期

页码：

收录情况： SCI

摘要： Multitemporal remote sensing data, especially those for key phenological periods, play an important role in crop classification. However, cloudy/rainy climate conditions can easily lead to a lack of valid optical data, leading to crop classification difficulties. A general solution is taking advantage of all-weather synthetic aperture radar (SAR) datasets. In practice, SAR and optical datasets are often applied in the agricultural field by the method of image fusion, but it is difficult to apply when the number of optical images is too small. To solve this problem, this research proposes a data-transfer and feature-optimize-based method, which deploy an RNN-based encoding-decoding network to add additional data to the 'optical' temporal features at the farmland parcel scale and improve the utilization of optical fragments. On the basis of this method, we mitigate inconsistencies in spatial scale among different datasets and optimize the time-series parameters without expert knowledge in the crop classification procedure. The experimental results illustrate the crop classification accuracy of this method, which achieves a 4.1% improvement over the traditional approach and is especially effective for dryland crops (e.g. corn and rapeseed). Thus, this research demonstrates the effectiveness of the combined use of optical and SAR data for similar applications in cloudy/rainy mountainous areas.

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