Sven Nussbaum

Automation of change detection procedures for nuclear safeguards-related monitoring purposes

Against the background of nuclear safeguards applications using commercially available satellite imagery, a two-steps attempt for change detection and analysis was realized in general. Beginning with the wide-area monitoring on the basis of medium-resolution satellite data for the pre-scanning of significant changes within the nuclear-related locations, the areas of interest could then be explicitly analyzed by change detection and analysis methods using high-resolution satellite data. The change pixels were detected by using the multivariate alteration detection (MAD) transformation, producing a set of mutually orthogonal difference images (the so-called MAD variates). The decision thresholds for the change pixels were set by applying a probability mixture model to the MAD variates based on an EM algorithm. By means of eCognition a second, object-oriented procedure was implemented in order to create an automated workflow for the multiscale extraction of the (change) objects and (change) features for the subsequent post-classification of the areas of interest. Regarding the necessity of automation for extensive monitoring tasks the processing aspects of standardization and transferability took the centre stage of the investigations.

Change Detection: The Potential for Nuclear Safeguards

An object-oriented monitoring system for nuclear safeguards purposes was proposed in order to detect changes within nuclear facilities. By means of pixel-based change detection and object-oriented post-classification by eCognition some investigations were carried out in terms of automation, thus standardization and transferability. As a result, medium-resolution imagery could be considered as suitably for change-/no change-analysis in terms of wide area monitoring, for the detailed object-oriented analysis of significant changes high-resolution satellite imagery should be used. The automation and the transferability of the change detection and analysis procedures appears to be feasible to a certain extent, therewith giving rough and fast indications of areas of interest and explicitly analyzing the relevant areas.

Nuclear Research Reactors

An introduction into the topic of nuclear research reactors (RR) is given in the preceding section on “Key Features of Nuclear Research Reactors”. This chapter deals with the object-based image analysis of two examples for RR. For these the applicability of the interpretation key developed in the previous chapters is investigated. The characteristic key features of objects for research reactors used here are a rectangular buildings measuring about 40m x 40m (for neutron beam production reactors). If the reactor is a pool-type reactor, then the building is cylindrical with a dome-shaped roof. Contiguous to the reactor is a long neutron guide hall measuring 30m x 50m.

Change detection using the object features

For the detection of changes, several statistical techniques exist. When adopted to high-resolution imagery, the results of the traditional pixel-based algorithms are often limited. Especially if small structural changes are to be detected, object- based procedures show promises. In the given paper, we propose an unsupervised object-based change detection and change classification approach based on the object features. Following the automatic pre-processing, image objects and their object features are extracted. Change detection is performed by the multivariate alteration detection (MAD), accompanied by the maximum autocorrelation factor (MAF) transformation. The change objects are then classified using the fuzzy maximum likelihood estimation (FMLE). Finally the classification of changes is improved by probabilistic label relaxation.

Change Detection Tools

In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches, multivariate alteration detection, hyperspectral approaches, and approaches that deal with changes between optical images and existing vector data. Radar methods include constant false-alarm rate detection, adaptive filtering, multi-channel segmentation (an object-oriented approach), hybrid methods, and coherent change detection. 3D methods focus on tools that are able to deal with 3D information from ground based laser-ranging systems, LiDAR, and elevation models obtained from air/space borne optical and SAR data. Highlighted applications are landcover change, which is often one of the basic types of information to build analysis on, monitoring of nuclear safeguards, third-party interference close to infrastructures (or borders), and 3D analysis. What method to use is dependent on the sensor, the size of the changes in comparison with the resolution, their shape, textural properties, spectral properties, and behaviour in time, and the type of application. All these issues are discussed to be able to determine the right method, with references for further reading

Object-based Image Analysis

With the availability of high-resolution satellite imagery, the use of remote sensing data has become very important for the verification of the nuclear safeguards agreement between a NPT Member State and the IAEA. Items and activities which can be monitored within nuclear facilities are, for instance, construction of buildings, plant expansion, changes of the operational status, and planning of underground activities. When applied to high-resolution imagery, traditional pixel-based image algorithms often yield limited results. Especially, if small structural objects are to be detected, object-based procedures are more appropriate. In comparison to solely spectral features applied within pixel-based approaches, utilization of object features, such as size or orientation of objects, their shape or texture and the relations between the objects in dierent scales, considerably extends the possibilities of image analysis. Analyzing satellite image data in an object-based method also oers the possibility to involve specific knowledge in the image classification or recognition process. This specific knowledge is here given through the “interpretation key”, presented in the previous chapters. This part deals with the computerbased interpretation of satellite and aerial images. Thereby the applicability of the “key” in the computer-based process is investigated. Figure 1 shows the advantage of a wide feature basis within an object-based classification. Here a subset of an Iranian Nuclear Power Plant is shown. With only the spectral values it is very dicult for a pixel-based classification to detect the heterogenous building in the top of the image and one will hardly be able the find out what kind of a building it is. Using objects provides us with a very wide feature base as mentioned above. So, a possible classification rule for this building could be: all objects within the image which are rectangular and have a special size (shape features), which have a given distance to the reactor dome (position feature) and have smaller rectangular objects on the roof (object-relation feature) are “Generator Halls”. The parameters for

Automated analysis of remote sensing data for extensive monitoring tasks in the context of nuclear safeguards

According to the expected technical improvements regarding the spatial and spectral resolution, satellite imagery could be more and more build the basis of complex systems in the future for recognizing and monitoring even small-scale and short-term structural features of interests within nuclear facilities, for instance construction of buildings, plant expansion, changes of the operational status,

Targeted information collection for nuclear verification: a combination of object-based images analysis and pixel-based change detection with very high resolution satellite data exemplified for Iranian nuclear sites

Since the availability of spatial high resolution satellite imagery, the use of remote sensing data has become very important for nuclear monitoring and verification purposes. For the detection of small structural objects in highresolution imagery recent object-based procedures seem to be more significant than the traditional pixel-based approaches. The detection of undeclared changes within facilities is a key issue of nuclear verification. Monitoring nuclear sites based on a satellite imagery database requires the automation of image processing steps. The change detection procedures in particular should automatically discriminate significant changes from the background. Besides detection, also identification and interpretation of changes is crucial. This paper proposes an new targeted change detection methodology for nuclear verification. Pixel-based change detection and object-based image analysis are combined to detect, identify and interpret significant changes within nuclear facilities using multitemporal satellite data. The methodology and its application to case studies on Iranian nuclear facilities will be presented.