Interactive models for semantic labeling of satellite images

We describe a system for interactive training of models for semantic labeling of land cover. The models are build based on three levels of features: 1) pixel level, 2) region level, and 3) scene level features. We developed a Bayesian algorithm and a decision tree algorithm for interactive training. The Bayesian algorithm enables training based on pixel features. The scene level summaries of pixel features are used for fast retrieval of scenes with high/low content of features and scenes with low confidence of classification. The decision tree algorithm is based on region level features that are extracted based on 1) spectral and textural characteristics of the image, 2) shape descriptors of regions that are created through segmentation process, and 3) auxiliary information such as elevation data. The initial model can be created based on a database of ground truth and than be refined based on the feedback supplied by a data analyst who interactively trains the model using the system output and/or additional scenes. The combination of supervised and unsupervised methods provides a more complete exploration of model space. A user may detect the inadequacy of the model space and add additional features to the model. The graphical tools for the exploration of decision trees allow insight into the interaction of features used in the construction of models. The preliminary experiments show that accurate models can be build in a short time for a variety of land covers. The scalable classification techniques allow for fast searches for a specific label over a large area.