Geometric-Harmonic Mean based Late Fusion Ensemble for Improved Weather Image Classification
Abstract
The present article introduces the Geometric Harmonic Ensemble (GHE) strategy for weather image classification. For varying perspective feature extraction, properties of transfer learned VGG16, Inception V3, and ResNet50 models have been exploited. GHE combines the geometric and harmonic means to balance the models’ consensus and divergence and is seen to achieve a superior classification accuracy of 92.82%, outperforming baseline ensembles on WEAPD dataset. Statistical analysis reveals significant differences favoring GHE across Precision, Recall, F1-Score, and Accuracy, with t-tests confirming the findings at a 0.05 significance level. GHE is also able to consistently outperform other competing methods, including state-of-the-art techniques.
Authors: Abhiroop Chatterjee, Dibyendu Chattoraj, Susmita Ghosh, Ashish Kumar Ghosh
Published in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2025)