Ground Penetrating Radar (GPR) systems allow the acquisition of images displaying the contents of the underground. Hence, GPR is used everywhere, where structures underneath the visible surface have to be investigated. Consequently, typical application fields are archeology and civil engineering, especially the detection of cables, pipes or other manmade objects.

GPR sensors can consist of one channel or of multiple channels, placed side by side. In the latter case, it is possible to acquire a two-dimensional image for each measurement, where the number of channels represents the number of columns in the image matrix.

Since a typical track of measurements often contains multiple of thousands GPR images, a visual analysis with focus on the detection of buried objects might be uneconomically. Moreover, due to its noisy characteristic in relation to the specific underground, it is often not easy to interpret GPR images immediately.

In this study, an unsupervised approach is presented, that provides both help for the visual analysis of GPR images and for the detection of potential buried objects. Therefore, it is usable to quickly generate or enlarge training datasets for machine learning approaches aiming at the analysis of GPR data.

As test data, several measuring tracks acquired by the multi-channel Stream C system at the site of Frankfurt University (GER) are available.

Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust.