Semi-supervised discriminative random field for hyperspectral image classification
Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semi-supervised learning (SSL) techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this work, we propose a new semi-supervised discriminative random field (SSDRF) technique for spectral-spatial hyperspectral image classification. The proposed approach is validated using a hyperspectral dataset collected using NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region. The obtained results indicate that, by automatically generating unlabeled information, the proposed SSDRF algorithm exhibits very good performance in terms of accuracies in comparison with supervised algorithms. In terms of computational cost, the proposed SSDRF algorithm self learns the classifier with the same complexity as the supervised algorithm, and converges very efficiently.