论文标题
通过卫星图像对云结构进行分类和理解
Classification and understanding of cloud structures via satellite images with EfficientUNet
论文作者
论文摘要
多年来，气候变化一直是一个共同的兴趣，也是关键政治讨论和决策的最前沿。浅云在理解地球的气候方面起着重要作用，但它们在气候模型中的解释和代表方面具有挑战性。通过对这些云结构进行分类，可以更好地了解云的物理结构，从而改善气候模型的产生，从而更好地预测气候变化或预测天气更新。云以多种形式组织起来，这使得构建传统的基于规则的算法以分离云特征的挑战。在本文中，使用新的卷积神经网络（CNN）进行云组织模式的分类，称为EfficityNet为编码器，将UNET用作解码器，它们可以用作功能提取器和细性粒度特征图的重建器，并用作分类器，并被用作分类器，这将帮助专家了解云将如何塑造未来的气氛。通过在分类任务中使用分割模型，可以表明，与UNET一起使用良好的编码器，可以从该数据集中获得良好的性能。 DICE系数已用于最终评估度量标准，在Kaggle竞争中，公共和私人和私人（测试集）排行榜的得分分别为66.26 \％和66.02 \％。
Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years. Shallow clouds play a significant role in understanding the Earth's climate, but they are challenging to interpret and represent in a climate model. By classifying these cloud structures, there is a better possibility of understanding the physical structures of the clouds, which would improve the climate model generation, resulting in a better prediction of climate change or forecasting weather update. Clouds organise in many forms, which makes it challenging to build traditional rule-based algorithms to separate cloud features. In this paper, classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN) named as EfficientNet as the encoder and UNet as decoder where they worked as feature extractor and reconstructor of fine grained feature map and was used as a classifier, which will help experts to understand how clouds will shape the future climate. By using a segmentation model in a classification task, it was shown that with a good encoder alongside UNet, it is possible to obtain good performance from this dataset. Dice coefficient has been used for the final evaluation metric, which gave the score of 66.26\% and 66.02\% for public and private (test set) leaderboard on Kaggle competition respectively.
