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

arXiv: https://arxiv.org/abs/1802.03518 · HTML: https://mlsanity.com/paper/1802.03518/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2018.

## Built on by (most cited first, 12 of 12)

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- [A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2211.02820/) (2022)
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- [Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network](https://mlsanity.com/paper/2206.13392/) (2022)
- [Towards a Universal Gating Network for Mixtures of Experts](https://mlsanity.com/paper/2011.01613/) (2020)
- [Measuring economic activity from space: a case study using flying airplanes and COVID-19](https://mlsanity.com/paper/2104.10345/) (2021)
- [Ensemble Learning techniques for object detection in high-resolution satellite images](https://mlsanity.com/paper/2202.10554/) (2022)
- [Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification](https://mlsanity.com/paper/2303.11389/) (2023)
- [Less yet robust: crucial region selection for scene recognition](https://mlsanity.com/paper/2409.14741/) (2024)

## Abstract (arXiv)

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