Training general representations for remote sensing using in-domain knowledge
This work addresses the challenge of efficient model training for remote sensing applications, though it is incremental as it builds on existing representation learning methods.
The paper tackled the problem of learning general representations for remote sensing to improve transfer learning accuracy and reduce training data needs, finding that using in-domain data significantly enhances performance, with gains of up to 11% and 40% compared to baselines at 100 training samples.
Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples. This paper investigates development of generic remote sensing representations, and explores which characteristics are important for a dataset to be a good source for representation learning. For this analysis, five diverse remote sensing datasets are selected and used for both, disjoint upstream representation learning and downstream model training and evaluation. A common evaluation protocol is used to establish baselines for these datasets that achieve state-of-the-art performance. As the results indicate, especially with a low number of available training samples a significant performance enhancement can be observed when including additionally in-domain data in comparison to training models from scratch or fine-tuning only on ImageNet (up to 11% and 40%, respectively, at 100 training samples). All datasets and pretrained representation models are published online.