A conjugated and augmented dictionary learning method for hyperspectral image classfication

A Conjugated and Augmented Dictionaries (CAD) learning method based on Sparse Auto-Encoder (SAE) is proposed for hyperspectral image classification. The CAD originates from the intention to combine the synthesis model and analysis model. These two models are used to obtain the sparse representation or feature of the pixels. In this paper, CAD has a three-step strategy to learn the dictionaries and classify the pixels of Hyperspectral image. Firstly, we adopt the Sparse Auto-Encoder model to complete the learning process of the suggested dictionaries. Secondly, test samples are reconstructed using the learned dictionaries. Finally, we embed the reconstructed pixels into a linear SVM for classification. Indiana Pine subset is used for the classification experiment, and the classification results show that the reconstructed pixels have the high discrimination characteristics, which makes our method outperforms other hyperspectral image classification algorithms as contrast.