were acquired before and after an attack for our change
analysis.
A fast detection and visualization of change in areas of crisis or
catastrophes are important requirements for planning and
coordination of help. Therefore, the objective of our research
was to develop a reliable and accurate automated algorithm to
detect changes on man-made objects. This algorithm should be
used in catastrophic events or humanitarian crises to show the
impact of this particular event..
2. STANDARD CHANGE DETECTION METHODS
For a comprehensive assessment of the quality of any new
method it is essential to compare it to the performance of
standard change detection approaches. For comparison, we
selected those algorithms that are available in most remote
sensing image processing systems. These methods are: (i)
image difference; (ii) image ratio; (iii) PCA; (iv) delta cue; and
(v) post classification analysis.
Image difference is an easy-to-understand and to-implement
method. It is based on calculating the per-pixel gray value
differences. If the resulting values are unchanged or do not
exceed a pre-determined threshold no change has occurred. The
degree of change is determined by the gray value differences.
The image ratio method is very similar to image difference. For
every pair of gray values at the same location at dates Tl and
T2 the per-pixel ratio of the two values is calculated. Both
methods vary through different spectral band combinations, the
choice of thresholds, or different available spectral resolutions
(Jensen 2005).
The principal component (PC) transform is a statistical method
to calculate a new synthetic (uncorrelated) data space. PC
analysis (PCA) can be used in different ways for change
detection. In this study, we employ a selective bitemporal PCA
(Tomowski et al. 2010). Two bitemporal spectral bands of the
same location are analyzed in a two-dimensional feature space.
As a result, all gray values are analyzed in relation to the two
principal components. Usually, the unchanged pixels lie in the
direction of the first PC whereas the changed pixel along the 2™
PC axis.
Post classification analysis is based on a comparison of two
independent classification results for at least two dates T1 und
T2. This method allows the determination of the kind of change
from one class to another.
The delta cue approach is a combination of different image
processing techniques. These techniques are assembled into an
integrated procedure. It consists of the following change
detection algorithms: (i) tasseled cap soil brightness and
greenness differences; (ii) magnitude difference; (iii) primary
color difference; (iv) single band difference; and (v) band slope
difference.
The following formula is used by all the presented change
detection algorithms to compute the relative difference (R) of
the images T1 and T2:
TI -T2 ,I-T2
|T1| |T2|
The features tasseled cap, primary color difference, band slope
difference, and magnitude difference cannot be used in this
study because the input images are panchromatic (single-band)
images. This leaves just the single band difference algorithm
and is therefore quite limited. In the next step, a threshold is
determined to differentiate between real change and pseudo
change. New geometric properties are then used to identify the
changed buildings. These geometric properties include area,
elongation, and compactness of connected pixels. These
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
connected pixels build a blob of which major and minor axis
can also be determined.
3. COMBINED EDGE SEGMENT TEXTURE (CEST)
ANALYSIS FOR CHANGE DETECTION
Based on the fact that simple methods such as image
differencing or image ratio failed to reliably detect changes of
buildings in the study images, we had to develop a different
procedure for automated change detection. This procedure is
based on a number of different principles, namely frequency
based filtering, segmentation, and texture analysis. Four of
these methods are based on filtering in the frequency domain
after a Fourier transform (FT), one on segmentation and the
others on texture features. The frequency domain is used
because it allows the direct identification of relevant features
such as edges of buildings. If no features are directly visible
(such as partial destruction with still standing outside walls),
texture parameters are used for debris identification. A
segmentation algorithm is used to extract size and shape of
buildings. These methods can be combined in a decision tree for
accuracy improvement. The combination of these processing
steps is called Combined Edge Segment Texture (CEST)
analysis.
3.1 Fourier Transform Based Algorithms
The FT is defined for a single band or panchromatic images
(Cooley & Tukey 1965). Based on a frequency analysis in the
spectral domain, isotropic band pass filters can be designed that
highlight selected frequencies and - as such - structures in the
images. The design of band pass filters in the frequency domain
is based on size and resolution of the images, and the estimated
size of buildings and man-made structures where changes are to
be detected. The filtered images are then transformed back into
the spatial domain for further analysis. Higher frequencies
visualize the position of building, the highest frequencies,
however, contain mostly noise and are not useful for object
identification and extraction. Lower frequencies contain mostly
general image background which is not used for further
analysis. After a number of tests, an optimum band pass filter is
created which includes the most appropriate information for
building extraction (Klonus et al. 2011b).
After transforming Tl and T2 via a fast FT (FFT) and the
adaptive band pass filtering, four different methods can be
applied to extract the changed structures: (i) subtraction in the
frequency domain, (ii) correlation in the frequency domain, (iii)
correlation in the spatial domain, and (iv) edge detection in the
spatial domain. Of these methods, the best results are obtained
using the edge detection algorithm (Klonus et al. 2011a).
Consequently, we incorporated this method as a default
function into the CEST analysis.
3.2 Methods Based on Texture Parameters
Frequency based filtering is particularly suited to detect
changes in edge structures. If edges remain intact, however,
textural features may be used for change analysis. For the
calculation of texture parameters, we make use of the well-
known features defined by Haralick et al. (1973). The idea is
that buildings can have higher texture values than areas without
buildings, especially, if the surrounding neighborhood is very
homogeneous and the buildings are very small or destroyed
(with surrounding debris). The Haralick features are calculated
using a window technique. Initial tests with a number of
different features showed that ‘energy’ and ‘inverse distance
