Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework

Abstract

The recent emergence of deep learning for characterizing complex patterns in remote sensing imagery reveals its high potential to address some classic challenges in this domain, e.g. scene classification. Typical deep learning models require extremely large datasets with rich contents to train a multilayer structure in order to capture the essential features of scenes. Compared with the benchmark datasets used in popular deep learning frameworks, however, the volumes of available remote sensing datasets are particularly limited, which have restricted deep learning methods from achieving full performance gains. In order to address this fundamental problem, this article introduces a methodology to not only enhance the volume and completeness of training data for any remote sensing datasets, but also exploit the enhanced datasets to train a deep convolutional neural network that achieves state-of-the-art scene classification performance. Specifically, we propose to enhance any original dataset by applying three operations – flip, translation, and rotation to generate augmented data – and use the augmented dataset to train and obtain a more descriptive deep model. The proposed methodology is validated in three recently released remote sensing datasets, and confirmed as an effective technique that significantly contributes to potentially revolutionary changes in remote sensing scene classification, empowered by deep learning.

Keywords

• deep learning
• remote sensing scene classification
• convolutional neural network (cnn)
• big data
• data augmentation