Few-Shot Learning For Remote Sensing Scene Classification

Scene classification has become an important research topic in remote sensing (RS) field. Typical solution relies on labeling a large enough set of the RS scenes manually using expert opinion if needed, then training the algorithm on this set to learn how to correctly classify other new scenes. The best performance deep learning models required a large labeled dataset for training. Accordingly, there is great need to develop intelligent machine learning algorithm that can learn to classify RS datasets containing new unseen classes from few labeled samples only. This problem is known as few-shot machine learning. In this work we develop a deep few-shot learning method for the classification of RS scenes. The proposed method is based on prototypical deep neural networks combined with SqueezeNet pre-trained CNN for image embedding. In this paper, we report preliminary results using the two RS scene datasets UC Merced and optima131.