期刊
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
卷 -, 期 -, 页码 2643-2646出版社
IEEE
DOI: 10.1109/IGARSS39084.2020.9324261
关键词
Convolutional neural network; AdaBoost; Scene classification; Remote sensing
类别
资金
- Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources
Deep learning is a powerful means to recognize remote sensing image scene categories. In this study, a deep convolutional neural network (CNN) based ensemble method is proposed. Firstly, a CNN architecture composed of the feature layer and the classifier layer is designed. Then the classifier layer of CNN is treated as base-learner and integrated with the AdaBoost technique to construct a CNN-AdaBoost ensemble framework. The proposed method is compared with the CNN-SVM and fine-tuned VGG16. The experiment results on UC Merced land-use dataset show that the CNN-AdaBoost achieves an improved overall accuracy by 4.46% against the sole CNN. Also, our method outperforms another two paradigms. Therefore, the proposed CNN based ensemble method is promising for image representations regarding remote sensing image scene classification.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据