An Analysis of the Gap Between Hybrid and Real Data for Volcanic Deformation Detection

Recently deep learning models were applied to detect fast short-term volcanic deformations using interferometric synthetic aperture radar (InSAR) data. However, volcanic deformation detection is limited by the availability of real positive samples. In previous work, we used hybrid synthetic-real InSAR deformation maps set to train an InceptionResNet v2 model capable of detecting deformations down to 5 mm/year in real set. However, our model also reported false positive detections. One possible reason is the data distribution gap between the real and hybrid sets. In this paper, an experiment is conducted to analyze the gap between the hybrid and real sets that resulted in false positives. Three subsets of the fine-tuning set are created based on t-SNE analysis using different sampling strategies. The classification model is fine-tuned using these subsets. The results show that the strategy of removing only the most confusing examples and keeping the larger data set size reduces the false positive rate from 32.29% to 27.01%.