  Training a modern deep neural network on massive labeled samples is the main
paradigm in solving the scene classification problem for remote sensing, but
learning from only a few data points remains a challenge. Existing methods for
few-shot remote sensing scene classification are performed in a sample-level
manner, resulting in easy overfitting of learned features to individual samples
and inadequate generalization of learned category segmentation surfaces. To
solve this problem, learning should be organized at the task level rather than
the sample level. Learning on tasks sampled from a task family can help tune
learning algorithms to perform well on new tasks sampled in that family.
Therefore, we propose a simple but effective method, called RS-MetaNet, to
resolve the issues related to few-shot remote sensing scene classification in
the real world. On the one hand, RS-MetaNet raises the level of learning from
the sample to the task by organizing training in a meta way, and it learns to
learn a metric space that can well classify remote sensing scenes from a series
of tasks. We also propose a new loss function, called Balance Loss, which
maximizes the generalization ability of the model to new samples by maximizing
the distance between different categories, providing the scenes in different
categories with better linear segmentation planes while ensuring model fit. The
experimental results on three open and challenging remote sensing datasets,
UCMerced\_LandUse, NWPU-RESISC45, and Aerial Image Data, demonstrate that our
proposed RS-MetaNet method achieves state-of-the-art results in cases where
there are only 1-20 labeled samples.
