  We describe in this paper Hydra, an ensemble of convolutional neural networks
(CNN) for geospatial land classification. The idea behind Hydra is to create an
initial CNN that is coarsely optimized but provides a good starting pointing
for further optimization, which will serve as the Hydra's body. Then, the
obtained weights are fine-tuned multiple times with different augmentation
techniques, crop styles, and classes weights to form an ensemble of CNNs that
represent the Hydra's heads. By doing so, we prompt convergence to different
endpoints, which is a desirable aspect for ensembles. With this framework, we
were able to reduce the training time while maintaining the classification
performance of the ensemble. We created ensembles for our experiments using two
state-of-the-art CNN architectures, ResNet and DenseNet. We have demonstrated
the application of our Hydra framework in two datasets, FMOW and NWPU-RESISC45,
achieving results comparable to the state-of-the-art for the former and the
best reported performance so far for the latter. Code and CNN models are
available at https://github.com/maups/hydra-fmow
