Automatic Meta-Feature Engineering for CNN Fusion in Aerial Scene Classification Task

The aerial scene-classification task is a challenging problem to remote sensing area with important applicability to civil and military affairs. A technique that has achieved excellent results in this task is the convolutional neural network (CNN). CNNs are powerful semantic-level feature-extraction techniques successfully applied to many application domains. Nevertheless, many works in the literature have shown that a single CNN cannot solve all kinds of application domains properly. Hence, an alternative solution might be the joining of CNN architectures as an ensemble of classifiers. In this sense, this letter proposes a new strategy of deep feature-based classifier fusion through a meta-feature engineering approach based on the Kaizen programming (KP) technique for the aerial scene-classification task. KP is a technique that continuously improves partial solutions and combines them into a complete solution. In the context, a partial solution is a meta-feature, and a complete solution is an ensemble of classifiers. In our experiments on three different public data sets, we show that KP can automatically engineer meta-features that significantly improve the performance of a stacked classifier while reducing the number of total meta-features.