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README.md

Representative-Discriminative Open-set Recognition

This is the implementation of the following paper:

R. Kaviani Baghbaderani, Y. Qu. H. Qi, C. Stutts, Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery, European Conference on Computer Vision (ECCV), 2020. [Slides]

Pre-requisites

  • Python 3.6
  • TensorFlow 1.15
  • Numpy 1.19
  • Scipy 1.5.1
  • Scikit-learn 0.23.1

Dataset

The code uses the following datasets:

  1. Pavia University (PaviaU)
  2. Pavia Center (Pavia)
  3. Indian Pines (Indian_pines)

Prepare the training dataset

To preprocess the Hyperspcetral data and divide it to Known and Unknown sets:

python preprocessing.py --dataset Indian_pines --unk 3 7

Training

To train the network on known set:

python train_rdosr.py --dataset Indian_pines

Testing

  1. To test the network on a combination of known and unknown sets:
python test_rdosr.py --dataset Indian_pines
  1. Run plot_loss_accu.m which will display the training curves.
  2. Run plot_histograms_ROC.m which will display the ROC curve.

Contact

Razieh Kaviani Baghbaderani (rkavian1@vols.utk.edu)

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Representative-Discriminative Open-set Recognition

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