Scalable semi-supervised classification of hyperspectral remote sensing data with spectral and spatial information

Semi-supervised learning using both labeled and unlabeled data is usually adopted to design a high-accuracy and robust classification system on small-size remote sensing training data set. As suggested in the machine learning literature, the larger amount of unlabeled patterns are used, the better classification accuracies can be obtained. Nevertheless, most recently proposed semi-supervised algorithms are unable to handle a large amount of unlabeled samples. In the paper, we present a scalable semi-supervised learning algorithm by using whole hyperspectral remote sensing image. In particular, both spectral features and spatial information of a remote sensing image are adopted for the scalable semi-supervised learning. The accuracy and the reliability of the proposed algorithm have been evaluated on the ROSIS university hyperspectral remote sensing image. The accuracies are better or comparable when compared to the supervised state-of-the-art algorithms on both small-size and the original training sets.