论文标题
小农场的农作物类型识别：卫星图像中的空间，时间和光谱分析
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery
论文作者
论文摘要
现代机器学习（ML）模型纳入遥感和农业的集成扩大了农业领域中卫星图像的应用范围。在本文中，我们介绍了从中期时空分辨率（MSTR）转变为高型矩分分辨率（HSTR）卫星图像时，作物类型鉴定的准确性如何提高。我们进一步证明，卫星图像中的高光谱分辨率可以改善低空间和时间分辨率（LSTR）图像的预测性能。与从HSTR图像获得的最佳结果相比，使用MSTR图像的多光谱数据时，F1得分增加了7％。同样，当使用基于农作物季节的多光谱数据的时间序列时，我们观察到F1得分的增加1.2％。结果激发了合成带产生领域的进一步进步。
The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution (HSTR) satellite images. We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images. Similarly, when crop season based time series of multispectral data is used we observe an increase of 1.2% in the F1-score. The outcome motivates further advancements in the field of synthetic band generation.
