Multilayer feature learning for polarimetric synthetic radar data classification

Features are important for polarimetric synthetic aperture radar (PolSAR) image classification. Various methods focus on extracting feature artificially. Compared with them, we have developed a method to learn feature automatically. The method is based on deep learning which can learn multilayer features. In this paper, stacked sparse autoencoder (SAE) as one of the deep learning models is applied as a useful strategy to achieve the goal. For improving the classification result, we use a small amount of labels to fine-tuning the parameters of the proposed method. Finally, a real PolSAR dataset is used to verify the effectiveness. Experiment result confirms that the proposed method provides noteworthy improvements in classification accuracy and visual effect.