Transferability and Generalization Investigation of Multiclass Cloud Masking Networks for unseen Biomes and Sensors - A Study on PlanetScope, Platero and Sentinel-2

With the rising amount of small satellite Earth observation missions, robust model transferability and generalization is becoming more important in satellite remote sensing image processing pipelines to enable a faster and more efficient processing setup. In
this work, the transferability and generalization capabilities of two multiclass cloud masking approaches are tested on the tropical rainforest biome in the Amazon, that is unknown to the models, and on two new satellite systems, Platero and Sentinel-2. This is developed as an examplary test, if the models can be used as a baseline for transfer learning and finetuning for new satellite mission cloud masking processors. The evaluation is on a qualitative level due to the lack of ground truth data and small sample size. The results are promising for the new biome but show that regionally occurring phenomena like snow can mislead the origin networks. Furthermore, the experiments show the importance of finetuning on datasets for new sensor systems, especially when facing high discrepancies in the spectral channels.