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.
Against the background of nuclear safeguards applications using
commercially available satellite imagery, procedures for wide-area
monitoring of the Iranian nuclear fuel cycle are investigated.
Specifically, object-oriented classification combined with
statistical change detection is applied to high-resolution
imagery. In this context, a feature recognition and analysis tool,
called SEaTH, has been developed for automatic selection of
optimal object class features for subsequent classification. The
application of SEaTH is presented in a case study of the NFRPC
Esfahan, Iran. The transferability of classification models is
discussed regarding the necessity for automation of extensive
monitoring tasks.
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