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We describe in this paper Hydra, an ensemble of convolutional neural networks(CNN) for geospatial land classification. The idea behind Hydra is to create aninitial CNN that is coarsely optimized but provides a good starting pointingfor further optimization, which will serve as the Hydra's body. Then, theobtained weights are fine-tuned multiple times with different augmentationtechniques, crop styles, and classes weights to form an ensemble of CNNs thatrepresent the Hydra's heads. By doing so, we prompt convergence to differentendpoints, which is a desirable aspect for ensembles. With this framework, wewere able to reduce the training time while maintaining the classificationperformance of the ensemble. We created ensembles for our experiments using twostate-of-the-art CNN architectures, ResNet and DenseNet. We have demonstratedthe 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 thebest reported performance so far for the latter. Code and CNN models areavailable at https://github.com/maups/hydra-fmow

Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification