Deep Feature Extraction Based On Siamese Network And Auto-Encoder For Hyperspectral Image Classification

Hyperspectral image classification with limited training samples has become a hot research topic recently. Though deep convolution neural network shows powerful ability for feature extraction, its good performance often relies on sufficient training data. In this paper, we propose a multi-task learning framework based on siamese network and auto-encoder to fully exploit limited labeled samples' information and obtain discriminative features for classification of hyperspectral images. A low intraclass and high interclass variability of features can be learned by metric learning using our framework. And superpixel-based 3D sample preprocessing is applied to improve the classification accuracy on the hyperspectral images' boundaries. The experimental results demonstrate that our framework can achieve competitive results compared with the state-of-the-art methods.