Abstract Critical infrastructures are the backbone of our societies with increasingly complex and networked characteristics and high availability demands. This makes them vulnerable to a wide range of threats that can lead to major incidents. Resilience is a concept that describes a system’s ability to absorb and respond to disturbances, as well as to learn from the past and anticipate new threats. In this article, we apply the Digital Twin concept to the infrastructure domain to improve the system’s resilience capabilities. We conduct a comprehensive requirements analysis related to infrastructure characteristics, crisis management and resilience measures. As a result, we propose a Digital Twin Conceptual Framework for critical infrastructures. We conclude that the Digital Twin paradigm is well suited to enhance critical infrastructure resilience.

In this paper we show that wide band radar polarimetry can be used for discrimination between surface laid AP mine and non-mine targets. To do this we employ two types of coherent radar decomposition theorem applied to wide band chamber measurement data from the European Microwave Scattering Laboratory (EMSL). Our main conclusion is that radar polarimetry should be further investigated as an important way to secure low false alarm rate from radar sensors in stand off mine detection.

Radar polarimetry is a well established technique for classification of land use features. Several investigations have reported the use of polarimetric data to map Earth terrain types and land covers and have advanced a good understanding of polarimetric scattering mechanisms. In this paper 3 different approaches are outlined. The first one is a neural network classification using entropy H and /spl alpha/-angle from the Cloude decomposition theorem (CDT) as input parameters. The second approach is an extended H-/spl alpha/-classification using not only the H and /spl alpha/ parameters but also the first eigenvalue. Finally an approach is discussed using the 6 eigenvalues from C-band and L-band. The main advantage of these methods is strong correlation between the results and the physical reality which makes this methods suitable for un-supervised and automatic classification. The potential of this methods is demonstrated using spaceborne data acquired during the 2nd SIR-C/X-SAR mission of the test site Oberpfaffenhofen, Germany (data take 30.00).

Classification of Earth terrain components within full polarimetric data sets is an important application of Radar Polarimetry. Several investigations have reported the use of polarimetric data to map earth terrain types and land covers [1],[2],[3] and have advanced a good understanding of polarimetric scattering mechanisms. In this paper an approach based on the Shane R. Cloude's Decomposition Theorem [1] is outlined. The Entropy H and alpha - angle Classification is extended by using not only the H and a parameters but also the first eigenvalue. The main advantage of these approaches is the strong correlation between the polarimetric results and the physical reality which makes this methods suitable for unsupervised and automatic classification. The potential of this methods is demonstrated using space borne data acquired during SIR-C/X-SAR mission as well as airborne SAR data in L-band from DLR's Experimental SAR (ESAR) system of the test site Oberpfaffenhofen, Germany. The classifications are geocoded and compared to ground data for validation purposes.

The investigation presented in this paper demonstrates the potential of the combination of polarimetric and interferometric classification techniques for the extraction of map relevant features from space borne SAR data. In the first part we discuss a polarimetric classification technique based on Cloude's decomposition theorem. Afterwards we demonstrate the abilities of interferometric classification. The complementarity of the polarimetric and interferometric coherence based classification approaches can be used to resolve ambiguities that remain if one method is applied alone. The improvements resulting from their combination are suitable for an automatic classification and extraction of cartographic relevant features from space borne SAR data.

The investigation presented in this paper demonstrate a first order approach to an automatic classification and extraction of cartographic relevant features from SAR data. The authors propose a fusion of polarimetric and interferometric classification techniques that is able to solve several classification ambiguities which are not resolvable with one method alone and is also able to improve significantly the accuracy of the classification results. The complimentarity of the polarimetric and interferometric coherence based classification approaches and the improvements resulting from their combination are demonstrated using data from the space-shuttle-borne SIR-C/X-SAR radar system.