In object-based change detection approaches using specified object features as change measures, segmentation
is the crucial step, especially when also shape changes are considered. In this paper we present an enhanced
segmentation procedure based on the multiresolution segmentation. The procedure segments the first image
using the multiresolution segmentation. The segmentation is then applied to the second image and checked for
its consistency. If a segment is found to be inconsistent with the second image, it is split up. The performance
of the proposed procedure is demonstrated based on simulated and real image data.
The paper presents some recent developments on object-based change detection and classification. In detail,
the following algorithms were implemented either as Matlab or IDL programmes or as plug-ins for Definiens
Developer: i) object-based change detection: segmentation of bitemporal datasets, change detection using the
Multivariate Alteration Detection1 based on object features; ii) object features and object feature extraction:
moment invariants, automated extraction of object features using Bayesian statistics; iii) object-based classification
by neural networks: FFN and Class- dependent FFN using five different learning algorithms. The paper
introduces the methodologies, describes the implementation and gives some examples results on the application.
Earth observation generally represents a key source of information for the different national and international
bodies involved in the implementation of international agreements. If the area of interest is not accessible,
remote sensing sensors represent one of the few opportunities to gather almost realtime data over the area.
Taking into consideration recent developments in satellite sensor technologies and software solutions, the given
paper discusses some challenges with regard to both technical and political issues.
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.
Commercial satellite images have long been used for environmental monitoring. The improvements in spatial and spectral resolution bring with them new applications in different fields. We have already investigated the use of medium-resolution LANDSAT TM5 images for the routine nuclear verification, based on recently published visualization and change detection algorithms: canonical correlation analysis to enhance the change information in the difference images and Bayesian techniques for the automatic determination of significant thresholds. Now, the high spatial ground resolution of IKONOS and other future satellites provides a good basis for recognizing and monitoring of small-scale structural changes and for planning of routine and/or challenge inspections of nuclear sites. Aside from the advantages of the improved spatial resolution some problems due to sensor and solar conditions exist: Shadow formation and off-nadir images make it more difficult to interpret the complex changes. In order to solve these problems, we supplement the pixel-based change detection analysis with a supervised, object-oriented post-classification of change images carried out with the image analysis system eCognition. Defining of different object classes of the change pixels helps to distinguish between the different man-made, vegetation and other changes. By means of semantic relations between the object classes of changes and other classes it is possible to exclude shadow affected regions and to concentrate on specific areas of interest.
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