Immersive Visual Information Mining for Exploring the Content of TerraSAR-X Archives

Kurzfassung

The amount of collected Earth Observation (EO) data is increasing intensively in order of several Terabytes a day. Consequently, new trends to explore and retrieve information from the available data is highly demanded. Recent proposed methods for exploring EO data are mainly based on Image Information Mining (IIM). Because the main steps of this approach are image features extraction, data reduction, and labeling, developing a new process chain, mainly based on human interaction, might be a promising solution. More precisely, this chain provides an active learning system by interacting human users with features descriptors in a virtual environment. The focus of this article is an Immersive Visual Information Mining in which feature descriptors/images are visualized in a 3-D virtual environment, so called CAVE. This environment also allows the users to manipulate the feature space to increase the performance of learning process in an interactive manner. In our experiments, we use a dataset of Synthetic Aperture Radar (SAR) images. To process the data, the contents of images are represented by three different feature descriptors comprising Gabor, adapted WLD, and Bag-of-Words.