Virtual Samples for Cloud Classification via Supervised Learning

机译：通过监督学习进行云分类的虚拟样本

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摘要

Convolutional neural networks (CNNs) have been widely used in image classification task, which is based on the huge amount of image samples. However, the insufficiency of cloud sample numbers brings obstacles to classify clouds using CNNs. In this paper, we propose to apply Wasserstein generative adversarial network (WGAN) to generate virtual cloud samples via supervised learning. Afterward, we fine-tune a deep CNN model to evaluate the classification performance under different number of virtual cloud samples. The experimental results demonstrate the feasibility of the proposed method.

机译：卷积神经网络（CNN）已被广泛用于基于大量图像样本的图像分类任务中。但是，云样本数量不足会给使用CNN进行云分类带来障碍。在本文中，我们建议应用Wasserstein生成对抗网络（WGAN）通过监督学习生成虚拟云样本。然后，我们微调一个深层的CNN模型，以评估在不同数量的虚拟云样本下的分类性能。实验结果证明了该方法的可行性。

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《International conference on communications, signal processing, and systems》|2018年|80-86|共7页
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Shuang Liu; Mei Li; Zhong Zhang; Mingzhu Shi; Xiaozhong Cao;
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Convolutional neural networks; Generative model; Discriminative model; Cloud classification;

机译：卷积神经网络生成模型;判别模型;云分类;

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机译：使用增强型KNN算法的文本分类中的有监督和半监督学习：文本分类中有监督和半监督分类的比较研究
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机译：后验分布学习（PDL）：一种新颖的监督学习框架，使用未标记的样本来提高分类性能
3. Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests [J] . Moorthy Sruthi M. Krishna, Calders Kim, Vicari Matheus B., IEEE Transactions on Geoscience and Remote Sensing . 2020,第5期

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4. Virtual Samples for Cloud Classification via Supervised Learning [C] . Shuang Liu, Mei Li, Zhong Zhang, International conference on communications, signal processing, and systems . 2020

机译：通过监督学习的云分类虚拟样本
5. Semi-Supervised Machine Learning Techniques for Classification of Evolving Data in Pattern Recognition =TECHNIQUES SEMI-SUPERVISéES D'APPRENTISSAGE MACHINE POUR LA CLASSIFICATION DES DONNéES EN éVOLUTION EN RECONNAISSANCE DE FORMES [D] . Tencer, Lukas. 2017

机译：半监督机器学习技术，用于模式识别中不断发展的数据分类=在表单识别中对数据进行分类的半监督机器学习技术
6. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning [O] . Victoria Plaza-Leiva, Jose Antonio Gomez-Ruiz, Anthony Mandow, 2017

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7. Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features [O] . Muhammed Enes Atik, Zaide Duran, Dursun Zafer Seker 2021

机译：基于机器学习的Point Clouds的监督分类使用多尺度几何特征

Virtual Samples for Cloud Classification via Supervised Learning

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