Few-Shot Remote Sensing Image Scene Classification Based on Variational Meta-Learning

As a typical meta-learning method, model-agnostic meta-learning has attracted widespread attention in the remote sensing field due to its flexibility and applicability to various problems. However, the diversity and uniqueness of meta-tasks are constrained by the influence of remote sensing datasets from different imaging devices, resolutions, and diverse application requirements, which affects the algorithm’s ability to handle different tasks. To address this issue, this paper introduces a new concept called ”central points” starting from the distribution of meta-parameters, within a variational meta-learning framework [7](VML). Based on the concept of central points, we propose a variational meta-learning framework (VML) to obtain ”task central points” through variational inference and generate model parameters from these task central points, enabling the model to adapt to different tasks. To evaluate the utility of VML, experiments were conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results demonstrate that compared to some common meta-learning methods, VML effectively addresses the issues of 1 task diversity and heterogeneity in remote sensing datasets, and improves the classification accuracy of remote sensing datasets.