The iteratively re-weighted multivariate alteration detection (IR-MAD) algorithm may be used both for unsuper-
vised change detection in multi- and hyperspectral remote sensing imagery as well as for automatic radiometric
normalization of multi- or hypervariate multitemporal image sequences. Principal component analysis (PCA) as
well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images,
both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change
background. The kernel versions are based on a dual formulation, also termed Q-mode analysis, in which the
data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products
of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature
space. Via kernel substitution, also known as the kernel trick, these inner products between the mappings are in
turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel
function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component
analysis (PCA), kernel MAF and kernel MNF analyses handle nonlinearities by implicitly transforming data into
high (even in¯nite) dimensional feature space via the kernel function and then performing a linear analysis in
that space.
In image analysis the Gram matrix is often prohibitively large (its size is the number of pixels in the image
squared). In this case we may sub-sample the image and carry out the kernel eigenvalue analysis on a set of
training data samples only. To obtain a transformed version of the entire image we then project all pixels, which
we call the test data, mapped nonlinearly onto the primal eigenvectors.
IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric normalization and
kernel PCA/MAF/MNF transformations have been written which function as transparent and fully integrated
extensions of the ENVI remote sensing image analysis environment. Also, Matlab code exists which allows for
fast data exploration and experimentation with smaller datasets. Computationally demanding kernelization of
test data with training data and kernel image projections have been programmed to run on massively parallel
CUDA-enabled graphics processors, when available, giving a tenfold speed enhancement. The software will be
available from the authors' websites in the near future.
A data example shows the application to bi-temporal RapidEye data covering the Garzweiler open pit mine
in the Ruhr area in Germany.
Kernel versions of the principal components (PCA) and maximum autocorrelation factor (MAF) transformations
are used to postprocess change images obtained with the iteratively re-weighted multivariate
alteration detection (MAD) algorithm. It is found that substantial improvements in the ratio of
signal (change) to background noise (no change) can be obtained especially with kernel MAF.
KEYWORDS: Principal component analysis, Data acquisition, Data modeling, Information science, Data centers, Aerospace engineering, Navigation systems, Sensors, Chemistry, Visualization
Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A
commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two
variables which represent the same spectral band covering the same geographical region acquired at two different
time points. If change over time does not dominate the scene, the projection of the original two bands onto the
second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the
analysis. Unlike ordinary PCA, kernel PCA with a Gaussian kernel successfully finds the change observations in
a case where nonlinearities are introduced artificially.
The iteratively re-weighted multivariate alteration detection (IR-MAD) transformation is proving to be very
successful for multispectral change detection and automatic radiometric normalization applications in remote
sensing. Various alternatives exist in the way in which the weights (no-change probabilities) are calculated
during the iteration procedure. These alternatives are compared quantitatively on the basis of multispectral
imagery from different sensors under a range of ground cover conditions exhibiting wide variations in the amount
of change present, as well as with a partially artificial data set simulating truly time-invariant observations. A
best re-weighting procedure is recommended.
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.
The statistical techniques of multivariate alteration detection, maximum autocorrelation factor transformation, expectation
maximization, fuzzy maximum likelihood estimation and probabilistic label relaxation are combined in a unified scheme to classify changes in multispectral satellite data. An example involving bitemporal LANDSAT TM imagery is given.
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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