论文标题
贝叶斯聚合改善了传统的单图作物分类方法
Bayesian aggregation improves traditional single image crop classification approaches
论文作者
论文摘要
机器学习（ML）方法和神经网络（NN）被广泛用于基于卫星图像的作物类型识别和分类。但是，这些研究中的大多数都使用了几个多颞图像，这些图像可能不适合多云地区。我们提供了经典ML方法与U-NET NN之间的比较，用于将农作物用单个卫星图像分类。结果表明，使用现场分类而不是像素方法。我们首先使用贝叶斯聚合进行现场分类，并在多数投票汇总之间提高了1.5％的结果。单卫星图像作物分类的最佳结果是实现梯度提升的最佳结果，总体精度为77.4％，宏F1得分为0.66。
Machine learning (ML) methods and neural networks (NN) are widely implemented for crop types recognition and classification based on satellite images. However, most of these studies use several multi-temporal images which could be inapplicable for cloudy regions. We present a comparison between the classical ML approaches and U-Net NN for classifying crops with a single satellite image. The results show the advantages of using field-wise classification over pixel-wise approach. We first used a Bayesian aggregation for field-wise classification and improved on 1.5% results between majority voting aggregation. The best result for single satellite image crop classification is achieved for gradient boosting with an overall accuracy of 77.4% and macro F1-score 0.66.
