Aerial scene classification in remote sensing presents a significant challenge due to high intra-class variability and the different scales and orientations of the objects within dataset images. While deep learning architectures are commonly used for scene classification tasks and in the remote sensing area, Deep metric learning (DML) offers a more adaptable solution for more challenging classification scenarios by learning the characteristics of each class. This study exploits the usage of DML approaches for aerial scene classification tasks, analyzing their behavior with different pre-trained Convolutional Neural Networks (CNNs), and their combination through evolutionary computation algorithms. Our experiments show that DML approaches can achieve better classification results as compared to traditional pre-trained CNNs for three well-known remote sensing aerial scene datasets. Furthermore, we found that using the Univariate Marginal Distribution Algorithm (UMDA) to construct the final ensemble of varied DML-based classifiers is essential for achieving consistency and high-accuracy results across all datasets, improving the state-of-the-art by over 5.6%.