| Popis: |
In machine learning, few shot learning techniques try to generate high prediction accuracy from few training examples. One of those techniques is data augmentation. In this paper, we propose a method for land cover image classification using a conditional generative adversarial network (cGAN) starting from few training samples. First, we train a cGAN with a small dataset to classify images by mapping each pixel of the input image to its class. Second, we augment the images of the dataset by generating new samples using a traditional GAN model, then the labels of the new samples are generated using the previously trained cGAN model. The new augmented labeled data is associated with the original dataset to train the cGAN model again with more samples to improve the classification accuracy. Training experiments were performed on SPARCS publicly available dataset. We tested the proposed method on images of Landsat 8 and Alsat 2 satellites. The classification results show that the performance after data augmentation is better than using the original small dataset. |
