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Comparison of Deep Learning Model Performance between Meta-Dataset Training Versus Deep Neural Ensembles

Recently, many high-resolution remote sensing imagery (HR-RSI) datasets have been released that have diverse characteristics, such as high inter-class and low intra-class variation. Additionally, a benchmark meta-dataset (MDS) was created by agglomerating object classes from multiple HR-RSI datasets. Previous work has shown that deep convolutional neural networks (DCNN) trained on the MDS perform on par with DCNN trained on constituent benchmark datasets in cross-validation experiments. Here we train a model ensemble on four datasets and compare it with a single robust DCNN trained on the MDS. The goal is to better understand under what conditions an ensemble of models, each trained with distinct datasets, is advantaged or disadvantaged compared to a single model trained with the agglomerated MDS for classification performance.