Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification
- Rodrigo Minetto - Universidade Tecnológica Federal do Paraná (UTFPR)
- Mauricio Pamplona Segundo - Universidade Federal da Bahia (UFBA)
- Sudeep Sarkar - University of South Florida (USF)
This research was conducted while the authors were at the Computer Vision and Pattern Recognition Group, USF.
Functional Map of the World description
The fMoW challenge consists of creating automatic solutions to classify a specific given location as one of the 62 target classes (e.g. airport, flooded road, nuclear power plant and so on) or as none of them (false detections). It is sponsored by the Intelligence Advanced Research Projects Activity (IARPA), an organization within the Office of the USA Director of National Intelligence. fMoW images vary in quality and are distributed over more than 100,000 globe locations, which leads to high intraclass variations and considerable interclass confusion. This, added to traditional satellite imaging problems like viewpoint, weather, shadow and scale variations, makes this classification problem a lot harder than previous land use datasets, such as UC Merced Land Use Dataset, WHU-RS19 and NWPU-RESISC45. Finally, the fMoW challenge also limits time and computational resources for training and testing to minimize the disparity among participants' solutions.
Hydra is a framework that creates ensembles of Convolutional Neural Networks (CNN) for land use classification in satellite images. 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 to form an ensemble of CNNs that represent the Hydra's heads. The Hydra framework tackles one of the most common problem in multiclass classification, which is the existence of several local minima that prioritize some classes over others and the eventual absence of a global minimum within the classifier search space. The ensemble ends up expanding this space by combining multiple classifiers that converged to local minima and reaches a better global approximation. To stimulate convergence to different end points, we exploit different strategies, such as using online data augmentation, variations in the size of the region of interest, and different image formats released by the fMoW challenge. The classifiers employed in our Hydra framework are variations of the fMoW baseline code.
- Keras with TensorFlow backend
Download the fMoW-rgb dataset and uncompress it. To run our fMoW submission for a test set without running the training, execute the following sequence of commands:
$ git clone https://github.com/maups/hydra-fmow $ cd hydra-fmow $ docker build -t <id> . $ nvidia-docker run -v /path_to/fMoW-rgb/:/data:ro -v /path_to/your_tmp_files/:/wdata -it <id> bash keras@IMAGE-ID:/src$ ./test.sh /data/test/ output.txt
To run training and testing, execute the following (make sure the paths to training and validation sets inside docker are /data/train and /data/val, respectively):
$ git clone https://github.com/maups/hydra-fmow $ cd hydra-fmow $ docker build -t <id> . $ nvidia-docker run -v /path_to/fMoW-rgb/:/data:ro -v /path_to/your_tmp_files/:/wdata -it <id> bash keras@IMAGE-ID:/src$ ./train.sh keras@IMAGE-ID:/src$ ./test.sh /data/test/ output.txt
You may use a different test set as long as the folder has the same file organization as the fMoW test set.