A Novel Robust Classification Method for Ground-Based Clouds
Aihua Yu, Ming Tang, Gang Li, Beiping Hou, Zhongwei Xuan, Bihong Zhu, Tianliang Chen
Abstract
Though the traditional convolutional neural network has a high recognition rate in cloud classification, it has poor robustness in cloud classification with occlusion. In this paper, we propose a novel scheme for cloud classification, in which the convolutional neural networks are used for feature extraction and a weighted sparse representation coding is adopted for classification. Three such algorithms are proposed. Experiments are carried out using the multimodal ground-based cloud dataset and the results show that in the case of occlusion, the accuracy of the proposed methods can be much improved over the traditional convolutional neural network-based algorithms.