Label-Consistent Transform Learning for Hyperspectral Image Classification

Abstract

This letter proposes a new image analysis tool called label-consistent transform learning. Transform learning is a recent unsupervised representation learning approach; we add supervision by incorporating a label consistency constraint. The proposed technique is especially suited for hyperspectral image classification problems owing to its ability to learn from fewer samples. We have compared our proposed method with the state-of-the-art techniques such as label-consistent K-singular value decomposition, stacked autoencoder, deep belief network, convolutional neural network, and generative adversarial network. Our method yields considerably better results than all the aforesaid techniques.