Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification
This work addresses geospatial land classification for remote sensing applications, but it is incremental as it builds on existing ensemble and CNN methods.
The authors tackled geospatial land classification by proposing Hydra, an ensemble of convolutional neural networks that reduces training time while maintaining performance, achieving results comparable to state-of-the-art on the FMOW dataset and the best reported performance on the NWPU-RESISC45 dataset.
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