ISPRS, Vol.34, Part 2W2, "Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
279
oyu Road, Wuhan,
t regional or global
problem in remote
/arious geo-spatial
is the central part
ture-based image
change accuracy
9 detection, which
s separately and
-by-pixel basis to
Direct Multi-date
mages of the two
ation between two
the whole change
lassification. The
tntages over the
itly recognize the
:urred .robustness
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poral and/or multi-
include greater
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/ and difficulties
' assessment on
s generation of an
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Consequently ,the
ction methods is
a ground truth is
upervised change
ice image”. These
ge ratio, change
nalysis(PCA) and
k .morphological
mathematics. They process the two multi-spectral images
acquired at two different dates in order to generate the further
image. The computed difference image is such that the values
of the pixels associated with land cover changes present
values significantly different from those of the pixels
associated unchanged areas. The changed areas are then
identified by analyzing the difference image. For example, the
image difference technique generates the difference image by
subtracting pixel by pixel, a single spectral band of the two
multi-spectral images under analysis. The choice of the
spectral band depends on the specific type of change to be
detected. An analogous concept is applied by the widely used
change vector analysis (CVA) technique. In this case, each
pairs of corresponding pixels is represented by two vectors in
feature space called change vectors .The change vector takes
the difference between the feature vectors at the two times.
The magnitude of the change vector represents the degree of
change, while the direction of the change vector indicates the
types of change with the help of supervision on the change
types. In spite of their relative simplicity and widespread use,
the aforementioned change detection techniques exhibit a
major drawback : a lack of automatic and non-heuristic
techniques for the analysis of the difference image. In fact, in
classical techniques, such an analysis is performed by
thresholding the difference image according to the empirical
strategies or manual trial-and-error procedures, which
significantly affect the reliability and accuracy of the final
change detection map. Although many analysts proposed a lot
of automatic threshold selection methods
[Rosin,1999;Bruzzone 1999],they are only suitable for some
specific situations not for common use.
2.Problems & Requirements
There are eleven major problems associated with the
current change detection techniques.
i) Lack of theoretical basis for change detection is the
key problem. Many change detection techniques can detect
some change information in some specific situations, but when
the situations changed the results changed. In fact, because of
complexity of image problems, it is difficult to illustrate one kind
of universal truths.
ii) This is the further step for the first problem. Even if we
have no universal theory for change detection, it is practical
that we have some criterion for selecting different change
detection techniques according to different situations. But this
point is still not achieved.
iii) Most change detection techniques are based on pixel
level. But general speaking, a mere thresholding of the
difference signal obtained from two corresponding pixels was
insufficient to distinguish between changes of interest. So
some feature-based algorithms should be developed for
improving the reliability and accuracy of detection.
iv) We have too little information about spectral
characteristics of ground objects. This affects our
understanding to images. Of course this task is very time-
consuming and expensive.
v) Often we have no good methods for processing the
bad effects on image such as uncertain atmosphere
conditions, sensor noises, radiometric differences and so on.
These factors causes the low accuracy of change detection.
vi) Considering how humans detect changes from
images, it is obvious that very limited information and/or
knowiedge (about sensors, images, spatial relations and so
on) is utilized in current change detection techniques.
vii) Finding change (i.e. the amount of change detected)
is one of the most important objectives in change detection
applications, but most of the current change detection
techniques need a user-specified threshold which is often set
empirically and subjectively since there is theoretical guidance
to this problem.
viii) In most change detection techniques, the
dependency information between the two images is ignored.
ix) Only very limited or no information at all about the
direction and characteristics of actual changes occurred on the
ground can be induced using most current change detection
techniques [Xiaolong Dai, 1998]
x) One practical problem with difference image is that the
images are not in perfect spatial registration before analyzing
so the difference image will contain artifacts caused by
incomplete cancellation of the unchanged background objects.
This registration noise causes problems for most change
detection algorithms [Xiaolong Dai, 1998].
xi) Most techniques are not fully automated and some
are even non-quantitative.
In summary, many interactive change detection
techniques are in practice today. However, the majority of the
techniques themselves can only provide the binary change
mask and classification procedure must be applied to the
individual multi-temporal images in order to obtain the
categorical information of multi-date land covers. Besides,
