Training general representations for remote sensing using in-domain knowledge

• URL: http://arxiv.org/abs/2010.00332v1
• Date: Wed, 30 Sep 2020 15:00:07 GMT
• Title: Training general representations for remote sensing using in-domain knowledge
• Authors: Maxim Neumann, Andr\'e Susano Pinto, Xiaohua Zhai, and Neil Houlsby
• Abstract summary: This paper investigates development of generic remote sensing representations. It explores which characteristics are important for a dataset to be a good source for representation learning.
• Score: 23.741188128379893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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