Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification

Usually, image classification methods have supervised or unsupervised learning paradigms. While unsupervised methods do not need training data, the meanings behind the classified elements are not explicitly know. Conversely, supervised methods are able to provide classification results with an intrinsic meaning, since a labeled dataset is available for training, which may be a limitation in some cases. The semi-supervised learning paradigm, which simultaneously exploits both labeled and unlabeled data, may be an alternative to this dilemma. This work proposes a semi-supervised classification framework through the combination of the Hierarchical Divisive Algorithm and stochastic distance concepts, where the former is adopted to automatically determine clusters in the data and the latter is used to label such clusters in a supervised way. In order to verify the potential of the proposed framework, two case studies about land use and land cover classification were carried out in an Amazonian area using synthetic aperture radar and multispectral data acquired by ALOS PALSAR and LANDSAT-5 TM sensors. Supervised methods based on statistical concepts were also included in these studies as baselines. The results show that when very small training sets are available, the proposed method provides results up to 14.6% and 3.8% more accurate than the baselines with respect to the classification of TM and PALSAR images, respectively.