Multi-Scale Meta-Learning-Based Networks for High-Resolution Remote Sensing Scene Classification

High-resolution remote sensing (HRRS) image scene classification based on limited data set is challenging in practical application. Although convolutional neural networks have shown powerful feature representation capability, they cannot perform well in the absence of rich label information in general. This paper proposes a multi-scale meta-learning-based (MSML) model to complete the HRRS scene classification with a little labeled data. First, we develop a multi-scale feature learning strategy to explore the rich information from HRRS scenes. Then, to use small data to train our network, we formulate the meta-learning as a regularization term and embed it into the classification loss function. By optimizing the proposed loss function, we can obtain a robust and generalized scene classification model. The positive experimental results counted on a public HRRS scene data set show that our MSML model is useful in HRRS scene classification tasks.