AIFS-DATASET for Few-Shot Aerial Image Scene Classification

Few-shot learning (FSL), which aims to rapidly recognize unseen categories with limited samples, has attracted wide attention in aerial image scene classification. However, the existing methods generally train and evaluate the model within a dataset, and changing the dataset requires retraining and evaluation, which only realizes the generalization of intra-dataset. Considering meta-learning, this brings in a natural assumption: FSL should learn meta-knowledge from cross-domain heterogeneous tasks and then can generalize to new data distributions (e.g., datasets) with few samples. To this end, we propose a new benchmark, dubbed aerial image few-shot dataset (AIFS-DATASET), which is composed of diverse datasets and can provide more realistic heterogeneous task distributions. On AIFS-DATASET, we use many heterogeneous tasks, across multi-domains without any aerial image category, to train the model, achieving 'see more.' Then we transfer the learned knowledge to new tasks in aerial images to evaluate the generalization performance of the model, thus acquiring a 'well-informed' few-shot aerial image scene classification model. Moreover, the challenges of inter-class similarity and intra-class discrepancy in aerial images still exist. We also develop a dual constrained distance metric learning (DC-DML) framework to deal with the variable learning tasks adaptively and to achieve compact data distribution within a class and clear distribution gaps between classes from the perspective of metric learning. DC-DML mainly uses a task-adapted feature extractor while devising a novel distance metric with a cross-class bias penalty. By conducting experiments on AIFS-DATASET, we observed that DC-DML outperforms the current prevailing FSL approaches by a large margin.