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ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238
Object-based classification of remote sensing data
for change detection
Volker Walter*
Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24 D, Stuttgart D-70174, Germany
Received 31 January 2003; accepted 26 September 2003
Abstract
In this paper, a change detection approach based on an object-based classification of remote sensing data is introduced. The
approach classifies not single pixels but groups of pixels that represent already existing objects in a GIS database. The approach
is based on a supervised maximum likelihood classification. The multispectral bands grouped by objects and very different
measures that can be derived from multispectral bands represent the n-dimensional feature space for the classification. The
training areas are derived automatically from the geographical information system (GIS) database.
After an introduction into the general approach, different input channels for the classification are defined and discussed. The
results of a test on two test areas are presented. Afterwards, further measures, which can improve the result of the classification
and enable the distinction between more land-use classes than with the introduced approach, are presented.
D 2003 Elsevier B.V. All rights reserved.
Keywords: change detection; classification; object-oriented image analysis; data fusion
1. Introduction the real world is very small compared with the number
In Walter and Fritsch (2000), a concept for the
automatic revision of geographical information sys-
tem (GIS) databases using multispectral remote sens-
ing data was introduced. This approach can be
subdivided into two steps (see Fig. 1). In a first step,
remote sensing data are classified with a supervised
maximum likelihood classification into different land-
use classes. The training areas are derived from an
already existing GIS database in order to avoid the
time-consuming task of manual acquisition. This can
be done if it is assumed that the number of changes in
0924-2716/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.isprsjprs.2003.09.007
* Tel.: +49-711-121-4091; fax: +49-711-121-3297.
E-mail address: [email protected] (V. Walter).
of all GIS objects in the database. This assumption is
justified because we want to realise update cycles in
the range of several months.
In a second step, the classified remote sensing data
have to be matched with the existing GIS objects in
order to find those objects where a change occurred, or
which were collected wrongly. We solved this task by
measuring per object the percentage, homogeneity, and
form of the pixels, which are classified to the same
object class as the respective object stored in the
database (Walter, 2000). All objects are classified into
the classes fully verified, partly verified, and not found
by using thresholds that can be defined interactively by
the user.
The problem of using thresholds is that they are
data-dependent. For example, the percentage of veg-
Fig. 1. Pixel-based classification approach.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238226
etation pixels varies significantly between data that
are captured in summer or in winter. Other influencing
factors are light and weather conditions, soil type, or
daytime. Therefore, we cannot use the same thresh-
olds for different datasets. In order to avoid the
problem of defining data-dependent thresholds, we
introduce an object-based supervised classification
approach. The object-based classification works in
the same way as a pixel-based classification (see
Fig. 2), with the difference that we do not classify
each pixel but combine all pixels of each object and
classify them together. Again, the training areas for
the classification of the objects are derived from the
existing database in order to avoid a time-consuming
manual acquisition.
In a ‘‘normal’’ classification, the greyscale values
of each pixel in different multispectral channels and
possibly some other preprocessed texture channels are
used as input. For the classification of groups of
pixels, we have to define new measures that can be
very simple (e.g., the mean grey value of all pixels of
an object in a specific channel) but also very complex,
like measures that describe the form of an object. This
approach is very flexible because it can combine very
different measures for describing an object. We can
even use the result of a pixel-based classification and
count for each object the percentage of pixels that are
classified to a specific land-use class.
Because the result of the approach is a classifica-
tion into the most likely class, the problematic part of
matching is now replaced by a single comparison of
the classification result with the GIS database without
using any thresholds.
1.1. Related work
This kind of approach is an object-oriented image
analysis that is also successfully applied to other
Fig. 2. Differences between object-based and pixel-based classification.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 227
problems. A good overview of different approaches
can be found in Blaschke et al. (2000). These
approaches can be subdivided into approaches that
use existing GIS data to superimpose it on an image
(per-field or per-parcel classification), and approaches
that use object-oriented classification rules without
any GIS input. Approaches that use existing GIS data
are not very widely used today. In Aplin et al. (1999),
an example for a per-field classification approach is
introduced, which first classifies the image into
different land-use classes. Afterwards, the fields
(which represent forest parcels from a GIS database)
are subdivided into different classes, depending on
the classification result, by using thresholds. The
main difference of existing approaches compared
with our approach is that no thresholds are used in
our approach.
2. Object-based classification
2.1. Input data
The following tests were carried out with ATKIS
datasets. ATKIS is the German national topographic
and cartographic database, and captures the landscape
in the scale of 1:25,000 (AdV, 1988). In Walter
(1999), it was shown that a spatial resolution of at
least 2 m is needed to update data in the scale of
1:25,000. The remote sensing data were captured with
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238228
the DPA system, which is an optical airborne digital
camera (Hahn et al., 1996). The original resolution of
0.5 m was resampled to a resolution of 2 m. The DPA
system has four multispectral channels [blue 440–525
nm, green 520–600 nm, red 610–685 nm, near-
infrared (NIR) 770–890 nm].
2.2. Classification classes
Currently, 63 different object classes are collected
in ATKIS. There are a lot of object classes that can
have very similar appearances in an image of 2 m
pixel size (e.g., industrial areas, residential areas, or
areas of mixed use). Therefore, we do not use 63 land-
use classes for the classification but subdivide all
object classes into the five land-use classes: water,
forest, settlement, greenland, and roads. The land-use
Fig. 3. Input data for (a) object-based a
class roads is only used in the first step in the process
for the pixel-based classification. Because of the
linear shape, roads consist of many mixed pixels in
a resolution of 2 m and have to be checked with other
techniques (see Walter, 1998).
2.3. Input channels
Like in a pixel-based classification, we can use all
spectral bands as input channels. The difference is that
in the pixel-based classification, each pixel is classi-
fied separately, whereas in the object-based classifi-
cation, all pixels that belong to one GIS object are
grouped together. In order to analyse the spectral
behaviour of objects, we calculate the mean grey
value of each channel for all GIS objects. Fig. 3
shows as an example the original input data (b) and
nd (b) pixel-based classification.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 229
the mean RGB (red green blue) value (a) of each GIS
object. The result of the pixel grouping is like a
smoothing of the data. The spectral behaviour of the
objects is similar to the typical spectral behaviour of
the pixels. For example, forest areas are represented in
the green channel by dark pixel/objects, whereas
settlements are represented by bright pixel/objects.
This behaviour can be also seen in Fig. 4. The
scatterplots show the distribution of (a) the grey values
of settlement and forest pixels compared with the
distribution of (b) the mean grey value of settlement
and forest objects in the channels red and NIR. It can
be seen that the behaviour is similar but the separation
of the two classes becomes blurred because of the
smoothing effect. In the object-based classification, all
multispectral bands of the DPA camera system (blue,
green, red, and NIR) are used as input channels.
Fig. 4. Scatterplot of (a) p
Different land-use classes cannot be distinguished
only by their spectral behaviour but also by their
different textures. Texture operators transform input
images in such a way that the texture is coded in grey
values. In our approach, we use a texture operator
based on a co-occurrence matrix that measures the
contrast in a 5� 5 pixel window. Fig. 5 shows the
used texture operator in an example. The input image
is shown in Fig. 5a, the texture (calculated from the
blue band) in Fig. 5b, and the average object textures
in Fig. 5c. Settlements are represented with dark
pixels, greenlands with bright pixels, and forests with
middle grey pixels.
The variance of the grey values of the pixels of an
object is also a good indicator of the roughness of a
texture. Fig. 6 shows the calculated mean variance in
the blue band for all objects. Settlement objects have
ixels vs. (b) objects.
Fig. 5. (a) Input image, (b) texture blue band, and (c) average object texture.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238230
high variance, greenland objects have middle variance,
and forest objects have low variance. Fig. 7 shows the
behaviour of the variance in the different bands: blue,
green, red, and NIR. The best discrimination between
land-use classes using the variance can be seen in the
blue band. In the NIR band, all land-use classes have a
similar distribution, which makes discrimination in this
band impossible.
Vegetation indices are very often used in pixel-
based classification as an input channel to improve
the classification result. They are based on the spectral
behaviour of chlorophyll, which absorbs red light and
reflects NIR light. In our approach, we employ the most
widely used normalised difference (Campbell, 1987):
VI ¼ IR� R
IRþ Rð1Þ
Fig. 8a shows the calculated vegetation index for
pixels and Fig. 8b for objects. It can be seen that
Fig. 6. Mean variance of GIS objects in blue band.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 231
settlements are represented typically by dark areas,
whereas forests are represented mostly by bright
areas. The classification of greenlands is difficult
because they can be represented by very bright areas
(e.g., fields with a high amount of vegetation) as well
as by very dark areas (e.g., fields shortly after the
harvest).
All so far defined input channels are also used in
‘‘normal’’ pixel-based classification. In object-based
classification, it is possible to add further input
channels, which do not describe directly spectral or
textural characteristics. For example, we can use the
result of a pixel-based classification and count the
percentage of pixels that are classified to a specific
land-use class. This evaluation is shown in Fig. 9. The
input image is shown in Fig. 9a and the pixel-based
classification result in Fig. 9b. Fig. 9c shows for each
object the percentage of pixels that are classified to
the land-use class forest. White colour represents
100% and black colour represents 0%. In Fig. 9b
and c, it can be seen that forest is a land-use class that
can be classified with high accuracy in pixel-based as
well as object-based classifications. Fig. 9d shows the
percentage of settlement pixels. Because of the high
resolution (2 m) of the data, settlements cannot be
detected as homogenous areas but they are split into
different land-use classes depending on what the
pixels are actually representing. Therefore, settlement
objects contain typically only 50–70% settlement
pixels in 2-m resolution images. This can be also seen
in Fig. 9e, which shows the percentage of greenland
pixels. Whereas greenlands contain up to 100% green-
land pixels, it can be seen that, in settlement areas,
pixels are also classified as greenlands.
An interesting visualisation of the feature space of
the object-based classification can be made with the
combination of three object-based evaluations of the
pixel-based classification. In Fig. 10, the percentage
of settlement pixels is assigned to the red band, the
percentage of forest pixels to the green band, and the
percentage of greenland pixels to the blue band of an
RGB image. The combination of these three bands
shows that the pixel-based classification of forests and
greenlands is very reliable, which can be seen on the
bright green and blue colour of the corresponding
objects. Settlement areas in contrast cannot be classi-
fied as homogenous areas. Therefore, settlement
objects are represented in a reddish colour that can
be brownish or purple.
3. Classification results
The approach was tested on two test areas (16 and
9.1 km2), which were acquired at different dates with
a total of 951 objects (194 forests, 252 greenlands,
497 settlements, and 8 water objects). The input
channels were:
� mean grey value blue band� mean grey value green band� mean grey value red band� mean grey value NIR band� mean grey value vegetation index� mean grey value texture from blue band� variance blue band� variance green band� variance red band
Fig. 7. Object variance in different bands (x-axis, variance; y-axis,
number of objects).
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238232
� variance NIR band� variance vegetation index� variance texture� percentage forest pixel� percentage greenland pixel� percentage settlement pixel� percentage water pixel.
The input channels span a 16-dimensional feature
space. All objects of the test areas are used as training
objects for the classification. That means that those
objects are also training objects that are wrong in the
database. In a manual revision, we compared the GIS
data with the images. The number of objects that were
not collected correctly, or where it was not possible to
decide if they are collected correctly without further
information sources is 63, which is more than 6% of
all objects. The average percentage of changes in
topographic maps in western Europe per year are
6.4% in scale 1:50,000, 7.4% in scale 1:25,000 and
8% in scale 1:1,000,000 (Konecny, 1996). Therefore,
the approach is robust enough if we want to update the
GIS database in 1-year cycles.
Fig. 11a shows the GIS data and Fig. 11b shows
the result of the object-based classification on a part of
one test area. Altogether, 82 objects (which are 8.6%
of all objects) were classified into a different land-use
class than the one assigned to them in the GIS
database.
These objects were subdivided manually into three
classes. The first class contains all objects where a
change in the landscape has happened and an update
in the GIS database has to be done. In this class, there
are 37 objects (45%). The second class contains all
objects where it is not clear if the GIS objects were
collected correctly. Higher-resolution data or some-
times even field inspections are needed to decide if the
GIS database has to be updated or not. In this class,
there are 26 objects (31%). The third class contains all
objects where the result of the classification is incor-
rect. In this class, there are 19 objects (23%).
4. Further work
The approach subdivides all objects into the classes
water, forest, settlement, and greenland. This can be
refined if more object characteristics are evaluated. In
Fig. 8. Vegetation index for (a) single pixels and (b) objects.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 233
the following, we suggest three possible extensions of
the approach.
4.1. Additional use of laser data
In Haala and Walter (1999), it was shown that the
result of a pixel-based classification can be improved
significantly by the combined use of multispectral and
laser data. Fig. 12 shows a pixel-based classification
result of a CIR (colored infrared) image with (b) and
without (c) the use of laser data as an additional
channel. The laser data improve the classification
result because they have a complementary ‘‘behav-
iour’’ to the multispectral data. With laser data, the
classes greenland and road can be separated very well
from the classes forest and settlement because of the
different heights of the pixels above the ground,
whereas in multispectral data, the classes greenland
and forest can be separated very well from the classes
roads and settlement because of the strongly different
percentages of chlorophyll. The four input channels,
which were calculated from the result of the pixel-
based classification (percentage forest pixels, percent-
age greenland pixel, percentage settlement pixels, and
percentage water pixels), are the channels with the
highest amount of influence for the object-based
classification. Therefore, the object-based classifica-
tion should also be improved by the combined use of
multispectral and laser data.
With laser data, further input channels can be
calculated like slope, average object height, average
object slope, etc. With high-density laser data, it could
be possible to distinguish, for example, between
residential areas and industrial areas. Fig. 13 shows
a laser profile (1 m raster width) of a residential area
(a) and an industrial area (b). In residential areas, there
are typically houses with sloped roofs and a lot of
vegetation between the houses, whereas in industrial
areas, there are buildings with flat roofs and less
vegetation. This characteristic can be described by a
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238234
two-dimensional evaluation of the slope directions of
each object and could be also useful to distinguish
between different types of vegetation.
The fusion of data from different sensors for
image segmentation is a relatively new field (Pohl
and van Genderen, 1998). The general aim is to
increase the information content in order to make the
segmentation easier. Instead of laser data, it could be
also possible to make a fusion with SAR data (e.g.,
see Dupas, 2000).
4.2. More texture measures
At the moment, we use a co-occurrence matrix,
mean variance, and mean contrast to describe the
texture of objects. These texture measures can be also
used in pixel-based classification by measuring the
variance and contrast of each pixel in an n� n
window. The problem of a window with a fixed size
is that mixed pixels at the object borders are classified
very often to a wrong land-use class. The larger is the
window, the more pixels will be classified wrongly.
This problem does not appear in object-based classi-
fication because we do not evaluate a window with a
fixed size but use the existing object geometry (in
order not to use mixed pixels at the object boarder, a
buffer is used and border pixels are removed). There-
fore, we suggest using more texture measures. Fig. 14
shows an example of a possible evaluation of the
texture. The images are processed with a Sobel
operator. Typically, farmland objects contain many
edges with one main edge direction (a), whereas in
forest objects, the direction of the edges is equally
distributed (b) and in settlement objects, several main
directions can be found (c). Other texture measures
could be, for example, the average length or contrast
of the edges. However, several tests have to be
performed in order to prove these ideas.
4.3. Use of multitemporal data
The main reason that the approach classifies
objects into a wrong class is that in practice, the
Fig. 9. Percentage right classified pixel. (a) Input image, (b) pixel-
based classification result, (c) percentage right classified forest pixels,
(d) percentage right classified settlement pixels, (e) percentage right
classified greenland pixels.
Fig. 10. Visualisation of the feature space of the object-based classification.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 235
appearance of objects can be very inhomogeneous. If,
for example, a settlement object contains large areas
of greenland but only few pixels that represent a
house or a road, it will be classified as greenland
and not as settlement. The object will be marked as an
updated object and an operator has to check the object
each time the data are revised because the approach
will classify the object every time as greenland.
A solution for this problem is to store all param-
eters of the n-dimensional feature space (mean grey
values, mean variance, etc.) of an object when it is
checked for the first time. If, then, later the object is
marked again as an update, the program can measure
the distance of the object in the current and the earlier
stored feature space. If the distance is under a specific
threshold, it can be assumed that the object is still the
same and therefore does not have to be updated.
5. Conclusion
The basic idea of the approach is that image
interpretation is not based only on the interpretation
of single pixels but on whole object structures. There-
fore, we do not classify only single pixels but groups
of pixels that represent already existing objects in a
GIS database. Each object is described by an n-
dimensional feature vector and classified to the most
likely class based on a supervised maximum likeli-
hood classification. The object-based classification
needs no tuning parameters like user-defined thresh-
olds. It works fully automatically because all infor-
mation for the classification is derived from
automatically generated training areas. The result is
not only a change detection but also a classification
into the most likely land-use class.
The results show that approximately 8.6% of all
objects (82 objects from 951) are marked as changes.
From these 82 objects, 45% are real changes, 31% are
potential changes, and 23% are wrongly classified.
That means that the amount of interactive checking of
the data can be decreased significantly. On the other
hand, we have to ask if the object-based classification
finds all changes. A change in the landscape can only
be detected if it affects a large part of an object
because the object-based classification uses the exist-
Fig. 12. (a) Input image, (b) classification with multispectral data, and (c) classification with multispectral and laser data.
Fig. 11. (a) GIS data and (b) result of the classification.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238236
Fig. 13. Laser profiles of (a) a residential and (b) an industrial area.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238 237
ing object geometry. If, for example, a forest object
has a size of 5000 m2 and in that forest object a small
settlement area with 200 m2 is built up, then this
approach will fail.
Further techniques have to be developed in order to
cover this problem. Because forest areas can be
classified very accurately in pixel-based classification,
it could be additionally tested whether there are large
areas in a forest object that are classified to another
Fig. 14. Different gradient directions for (a)
land-use class. The same approach could be used for
water areas because water is also a land-use class that
can be classified very accurately in pixel-based clas-
sification. More difficult is the situation for the land-
use classes greenland and settlement, which have
typically an inhomogeneous appearance in a pixel-
based classification. Here, we suggest using a multi-
scale approach to make additional verification of the
objects (e.g., see Heipke and Straub, 1999).
greenland, (b) forest, (c) settlement.
V. Walter / ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 225–238238
Up to now, we can only distinguish between the
land-use classes forest, settlement, greenland, and
water. This can be refined if more object character-
istics are evaluated. Some possible object character-
istics are defined in this paper and have to be tested in
future work.
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