Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model
In this work, we propose a new semi-supervised classification algorithm for remotely sensed hyperspectral images. The main contribution of this work is the development of new soft sparse multinomial logistic regression (S2MLR) model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. In order to obtain the soft labels, we use a recently proposed subspace-based MLR algorithm (MLRsub). The proposed semi-supervised algorithm represents an innovative contribution with regards to conventional semi-supervised learning algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with a widely used hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana. Our results indicate that the proposed method provides state-of-the-art performance when compared to other methods.