Transactions of Nanjing University of Aeronautics & Astronautics
Recognition of Similar Weather Scenarios in Terminal Area Based on Contrastive Learning
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
In order to improve the recognition accuracy of similar weather scenarios（SWSs） in terminal area， a recognition model for SWS based on contrastive learning （SWS-CL） is proposed. Firstly， a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images. Secondly， in the pre-trained recognition model of SWS-CL， a loss function is formulated to minimize the distance between the anchor and positive samples， and maximize the distance between the anchor and the negative samples in the latent space. Finally， the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS. The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset， and the proposed SWS-CL model can achieve satisfactory recognition accuracy. It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.
Keywords:
Project Supported:
This work was supported by the Fundamental Research Funds for the Central Universities （NOS.NS2019054， NS2020045）.
Clc Number:
TP391
Mobile website
