Transfer Learning for Cloud Image Classification
- Resource Type
- Conference
- Authors
- Jain, Mayank; Jain, Navya; Lee, Yee Hui; Winkler, Stefan; Dev, Soumyabrata
- Source
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :6005-6008 Jul, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Signal Processing and Analysis
Analytical models
Costs
Transfer learning
Satellite broadcasting
Neurons
Spatial resolution
Standards
Cloud Image Classification
Transfer Learning
Deep Learning
CNNs
VGG-16
- Language
- ISSN
- 2153-7003
Cloud image classification has been extensively studied in the literature, as it has several radio-meteorological and remote sensing applications. Recently, images from ground-based sky imagers (GSIs) are being widely used because of their high temporal and spatial resolution and low infrastructure cost as compared to satellites. To classify sky/cloud images obtained from such GSIs, this paper 1 examines the application of transfer learning using the standard VGG-16 architecture. The paper further analyzes the importance of adjusting the number of neurons in the top dense layers to improve the performance of the model. The reasons for the same are traced by conducting extensive experiments on multiple datasets exhibiting varied properties.