Urban Indicator for Database Updating
A Decision Tool to Help Stakeholders and Map Producers
Bénédicte Navaro, Zakaria Sadeq and Nicolas Saporiti
Geo212, 25 bis rue Jean Dolent, Paris, France
Keywords: Urban Areas, Object Detection, Spatial Databases, Minimum Noise Fraction, Supervised Learning,
Geodesic Dilation.
Abstract: The issue of regular spatial databases updating is partly solved by the abundance of satellite images. It is,
though, time consuming, requires qualified human resources, high financial costs and requests efficiency
(Bernard, 2007). This article presents a semi-automatic tool for urban detection, to guide the stakeholders
and the producers throughout the updating process. The industrial context of the study implies a fast,
instantaneous applicative workflow, operational on various landscapes with different sensors; it is thus
based on existing algorithms and software resources. The process is generic and adaptable, with a phase of
uncorrelation, chaining a Minimum Noise Fraction transformation with a textural analysis, a learning phase,
processed from an existing database, and an automatic modelling of the detected objects. The quantification
of the results shows the successful recreation of the existing database (90% of its surface) with a 7% rate of
potential big omissions. A specific highlight is made on the detection of disappeared buildings,
corresponding to 17.5% of the potential important omissions. This process has run in “real” updating
operations, on 1.5 and 6 meters resolution Spot6 images, a 15 meters Landsat-8 image and a 1.5 meters
resolution Pleiades image. A quantification of its results is also proposed in this study.
1 INTRODUCTION
A tremendous amount of techniques in the change
detection and database updating fields have already
been explored. Lu et al., 2003 “Change Detection
Techniques”, give an overview of the most common
techniques and qualify them in terms of
characteristics, advantages, disadvantages and key
factors. The heterogeneity of urban environments
and the large number of mixed pixels inherent
images often induced difficulty in urban land
use/cover classification based on spectral signature
(Lu and Weng, 2004). Recently, in the image change
detection field, much attention shifted to advanced
classification algorithms like neuronal network,
object-oriented and knowledge-based classification
approaches (Zhang and Wang, 2003). This study
aims at updating an existing database of urban GIS
objects with recent satellite imagery. The concept is
that, having assumed that the number of wrongly
detected GIS objects and the number of changes in
the real world are substantially less than the number
of all GIS objects of the data set, training areas can
be derived from existing GIS data (V. Walter, 2000).
As this study is realised in an industrial context and
aims at a generic and adaptable process, it is focused
on a simple chain processing, based on existing
algorithms. The easiest way to configure these
algorithms is to use the ones implemented in
software like ENVI and ArcGIS, available in the
company. These tools are not mandatory as the
image processing algorithms and statistical measures
they use, are well-known by the community and can
easily be reproduced in any computing languages.
The choice of a radiometric analysis is made due to
the simplicity of its implementation. The first phase
chains a Minimum Noise Fraction transformation
and a textural analysis resulting in two images: a
relevant component from the MNF transformation
and a grey scale image corresponding to the
previous component’s variance. The pixels’ values
in the two resulting images are combined with a
selection of relevant objects in the database to
establish a threshold. This learning phase finishes
with the morphological reconstruction of the
detected objects using a geodesic dilation with the
existing database as a mask.
Navaro, B., Sadeq, Z. and Saporiti, N.
Urban Indicator for Database Updating - A Decision Tool to Help Stakeholders and Map Producers.
DOI: 10.5220/0006327600810089
In Proceedings of the 3rd Inter national Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), pages 81-89
ISBN: 978-989-758-252-3
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
81
2 IMAGE PROCESSING
A multispectral Pleiades image (4 bands: blue,
green, red and PIR) from 2015/09/02, with a
2 meters resolution, is processed. The method aims
at extracting a specific thematic (urban objects) from
spectral information within a complex landscape
mixing vegetation, water, anthropogenic features,
soil ... To reduce the dimensionality of the image
and obtain a signal uncorrelated from the noise, a
Minimum Noise Fraction forward transform is
processed. The MNF transformation as modified
from Green et al. (1988) and implemented in ENVI,
is a linear transformation that consists of the
following separate principal components analysis
rotations (ENVI 4.2, 2005, Vermillion and Sader,
1999):
- The first rotation uses the principal
components of the noise covariance matrix to un-
correlate and rescale the noise in the data (a process
known as noise whitening), resulting in transformed
data in which the noise has unit variance and no
band-to-band correlation.
- The second rotation uses the principal
components derived from the original image data
after they have been noise-whitened by the first
rotation and rescaled by the noise standard
deviation.
The result of the MNF first rotation is a two
part data set, one part associated with large
eigenvalues and coherent eigenimages, and a
complementary part with near unity eigenvalues and
noise dominated images. The information is
compressed in different bands in which the
redundancy of the information is eliminated. In our
case, the majority of the information is contained in
the first 3 bands.
Then, the analysis of the spatial variation of a
component’s grey scale levels is processed: the
variance measures the dispersion of the values
around the mean. The solution implemented in
ENVI uses a co-occurrence matrix to calculate
texture values. This matrix is a function of both the
angular relationship and distance between two
neighbouring pixels. It shows the number of
occurrences of the relationship between a pixel and
its specified neighbour. Haralick et al. 1973 refer to
this as a “gray-tone spatial-dependence matrix”. The
texture analysis is done, in our case, on the second
band of MNF components (Figure 1).
(a)
(b)
(c)
Figure 1: Image processing results: (a) Pleiades 2m image
of Dire Dawa (Ethiopia), (b) MNF band 2, (c) Variance of
MNF band 2.
The chosen MNF component (band 2) and the
variance image are considered to be the resulting
images of the image processing phase. The learning
phase is applied on these two images.
3 LEARNING PROCESS
In order to calculate the threshold that will
discriminate urban objects in the resulting images
(Figure 1), the mean of the pixels’ values that are
located in areas labelled as “urban” amongst the
available database, is calculated.
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3.1 Labelling Objects as “Urban
Areas”
The selection of anthropogenic objects in the
database can be different regarding the type of the
available database. The positive learning in an
outdated database is justified by the rare
disappearance of urban objects. In the case of this
study, the buildings are represented individually so
the selection of a sample can easily be done in the
whole existing “building” layer. In other cases a
simple SQL request can sort the data by attributes
and randomly create fragments within the selection.
The choice of urban areas results from a human
consideration as it depends on the attributes the user
considers as “urban”. For example, urban places
with a lot of vegetation should not be inventoried
(like cemetery), nor punctual objects that are too thin
to be representative of the local area (like pylon or
water tanks). The choice of representative objects in
the database is, then, flexible, and can be updated
when changing area, if a new type of relevant
anthropogenic object appears. Nevertheless, the list
of these attributes has to be made just once for a data
model.
3.2 Thresholding
In order to establish a threshold, the mean of the
pixels’ values located in fragments of buildings, is
calculated on both images resulting from the image
processing (Figure 2).
Figure 2: Thresholding results based on the fragments (red
areas) in Dire Dawa, Ethiopia.
The final result consists in the intersection of
the two thresholded images and is presented in
Figure 3 over the original footprint of the buildings
(grey polygons).
Figure 3: Urban detections in a Pleiades image (2m) over
Dire Dawa (Ethiopia).
Figure 3 shows the multiple detections of
anthropogenic objects inside the city and the
differences with the database. These detections are
still blobs with undetermined shapes, so for them to
get the shape of the buildings, a step of
reconstruction is necessary.
4 MORPHOLOGICAL
RECONSTRUCTION
The geodesic dilation enables the reconstruction of
the buildings’ shapes. This is the dilation of an
image constrained by another image (Figure 4). The
first image is the assembly to dilate (marker) by a
structuring element, which is a 3 pixels square (this
is a size one dilation). The second image limits the
expansion of the dilation: it is the mask.
Figure 4: Blob extraction by marking and reconstruction,
Computer Vision (2015-2016), Marc Van Droogenbroeck.
In the example of Figure 4, even if the
marking blob only represents a small part of the
mask it is, thus, reconstructed.
In this study, the anthropogenic detections are
the blobs to reshape (red elements in Figure 5).
Blobs intersecting the existing database are the
markers to be dilated (blue elements in Figure 5),
Urban Indicator for Database Updating - A Decision Tool to Help Stakeholders and Map Producers
83
and the database itself is the mask (yellow polygons
in Figure 5).
Figure 5: Geodesic dilation of the MNF detections.
This reconstruction allows obtaining
maximum benefit from the MNF method, able to
detect small and isolated buildings. A certain
number of iterations lead to the reconstruction of the
buildings existing in the database and detected by
the MNF method (Figure 6).
Figure 6: Evolution of the geodesic reconstruction
(iteration from 1 to 185).
As the database used as a mask is outdated,
detections corresponding to new buildings are out of
the database: not all of the detections become
markers. So, this morphological reconstruction must
be completed by another step of reshaping. The
anthropogenic detections that have not been
reconstructed are reshaped.
5 AUTOMATIC RESHAPING
The automatic reshaping method is the one
imbedded in the ArcGIS solution: Minimum
Bounding Geometry.
The first step of the reshaping is the selection
of the MNF detections that have not been
reconstructed. This step is a simple GIS combination
of merging and intersection that enables to analyse,
for each MNF detection, if it is “well represented”
by the reconstruction. If the detection is covered by
the reconstruction polygon at a minimum of a 60
percent rate, it is, thus, considered as well
represented in the reconstruction. If not, the part of
the detection that is not covered at all by the
reconstruction is isolated and reshaped, as shown in
Figure 7.
(a)
(b)
Figure 7: Result of the reshaping (green polygons) from
the MNF detections (red blobs) after the reconstruction
step (yellow polygons).
Figure 7 illustrates the reshaping of a
detection that is not correctly represented in the
reconstruction step. Indeed, the blue circle shows an
extended building visible on the image that is not in
the existing database. The detection of this building
is reshaped, as are the isolated surrounding ones.
Notice that detections on the left side of the image
are not reshaped because they are considered as
“well represented” by the step of reconstruction
(yellow polygons).
Polygons smaller than 5 m² are cleared from
the results, considered as non significant.
6 RESULTS AND
QUALIFICATION
The compilation of the 3 steps (MNF detection,
morphological reconstruction and automatic
reshaping) leads to a global detection of buildings
whether they already exist in the database, or are
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new buildings (respectively yellow and green
polygons in Figure 8).
Figure 8: Building detections in Dire Dawa
The process enables to delete buildings
present in the database that do not exist anymore and
to complete the database with new buildings whether
they are simple extensions of buildings or obvious
urban expansion (green polygons in Figure 8).
6.1 Quantification with Individual
Buildings Composed Database
Out of the 58,473 buildings present in the database,
52,528 have been reconstructed which represent
89% of the original database (90% in surface).
Figure 9: Result of morphological reconstruction.
Out of the 5,945 non reconstructed objects,
93 % of them are small buildings with a surface
smaller than 100 m². In the 7 % of potential
omissions of big buildings, the analysis of every and
each building reveals (Figure 10) that nearly 75 % is
a real omission, but in 17.5 % of the cases, the
buildings that have not been reconstructed have
actually been destroyed.
Figure 10: Analysis of the non reconstructed buildings
which surface is over 100 m².
In terms of percentage, 11% of the initial database
has been omitted (Table 1), but significant omissions
only represent 0.23%.
Table 1: Error Type II considering the reconstructed
objects.
Figure 11 highlights buildings present in the
database (red polygons) that are not reconstructed
because they do not exist anymore in the recent
satellite image.
Figure 11: Non reconstruction of destroyed buildings (red
polygons).
Urban Indicator for Database Updating - A Decision Tool to Help Stakeholders and Map Producers
85
Detecting destroyed buildings allows to
rapidly point outdated objects to producers in the
context of rapid mapping.
One can notice that a large amount of
omissions is due to a tile coating of houses. Indeed,
this type of coating has a similar signal to vegetation
and is not representative of the majority of houses.
The learning process, as it uses a sample of
buildings, is based on the major type of coating
which is not tile. This problem is inherent to a
radiometry based analysis in a complex landscape.
An optional improvement step can be performed
using additional data (cf. 0 7 Improvement
step
).
An updated version of the database not being
available, it is complicated to estimate type I errors
concerning false positives. This would imply to
check each of the reconstructed and reshaped
polygons to validate their existence. This work has
been done on a 100 polygons sample to estimate the
quantity of false positives. Each polygon is checked
and qualified as :
- positive: the corresponding building is
clearly seen on the image. The shape of the polygon
matches the reality or makes a relevant envelope
around the buildings.
- false positive: no corresponding building on
the image.
- not identifiable: the polygon does not really
match the outline of the corresponding building. In
this case it is possible that the building is not the
detected element, but the detection is still relevant to
point out anthropogenic features (cars and elements
of the road are not comprised in these features).
The results are presented in Table 2.
Table 2: Error type I estimated on a sample polygons.
This analysis is made separately on the
polygons resulting from the phase of reconstruction
and the ones resulting from the phase of reshaping,
in order to estimate which one of the phases
produces more errors. Table 2 highlights that the
phase of reconstruction produces few errors of
commission (5%). Moreover, the mean size or false
positives in the reshaped buildings is around 30 m²
which is a small surface. These results may be
improved by changing the threshold of significant
surfaces applied at the end of the reshaping phase
(cf. 0).
6.2 Visual Qualification on Various
Areas
The robustness of the MNF method has been tested
with 3 different types of images (Spot6, Pleiades,
Landsat), with 3 different spatial resolutions (1.5 m,
6 m, 15 m), with different landscapes (desert, dense
urban area, mangrove, mine), on different swathes
and with different levels of the database’s
obsolescence. Each one of the 11 tests were realized
with the same settings: a unique list of representative
urban objects for the learning phase (no “building”
layer available, as in the Dire Dawa case), a variance
calculated from the second band of the MNF result,
and a size of fragment proportional to the pixel size.
And each one of them led to an enriched analysis of
the database’s obsolescence. Most of all, the method
has been tested in an operational context as an input
data for the map producers.
In Saint-Louis (Senegal) the method spotted
urban extensions, in Mali it detected a whole gold
mine (Loulo) that was absent from the database
(Figure 12). In the folowing figures the red marks
correspond to the indicator and the grey areas
correspond to the objects in the database.
(a)
(b)
Figure 12: Urban extensions in Saint-Louis, Senegal (a)
and detection of the Loulo gold mine in Mali (b).
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At a different scale, in Dubai and Bamako
new infrastructures and non-existing ones were
highlighted by superimposing the indicator on the
initial database (Figure 13).
(a)
(b)
Figure 13: New (circles) and obsolete (square)
infrastructures in Dubaï (a) and Bamako (b).
In addition of detecting the infrastructures,
the process limits the false detections which are, in
most of change detection methods, a limiting factor
of use. In Lu and Weng (2006) the impervious
surface was overestimated in the less-developed
areas but was overestimated in the well developed
areas. In our case, if the false detections still exist,
they are so few that they don’t perturb the visual
analysis of the indicator. Figure 14 illustrates the
ability of the process to concentrate the detections in
the urban area amongst a wide desertic study area.
(a)
(b)
Figure 14: Desertic areas with very few false detections in
Abeche (a) and Forchana (b), Chad.
Note that detections in (b) Figure 14 correspond to
small settlements.
7 IMPROVEMENT STEP
If the method gives good results, an improvement is
always possible. A choice of useful band is done
after the MNF transformation. It appears that the
urban information is not represented in just one
band. The detailed process can be performed on
another band or with additional data.
Surface elevation models provide a useful
additional information as it is independent of the
radiometric aspect of the data. With such a model,
the tile coating of the buildings, in the Dire Dawa
example, and the fact that its spectral signal is very
close to vegetation, is not a problem anymore. The
introduction of a geometric primitive (and not
radiometric) allows a more invariant detection of
buildings. Nevertheless, we chose not to work
initially with a surface elevation model as we
focused on the satellite image. Derivating such a
model from a stereo or tri-stereo satellite image is
thus possible, but is not the focusing point of our
work. Champion, 2011 proposes an innovative
database updating based on the improvement of a
Digital Terrain Model (DTM) derived from a Digital
Elevation Model (DEM).
In the case of Dire Dawa, DEM and DTM
data were available. The vegetation index (NDVI) is
calculated on the image to improve the distinction
between elevated objects corresponding to buildings
and other ones corresponding to trees. The elevation
data, masked from the vegetation, is then used in the
reconstruction step. In the first reconstruction step,
156 buildings with a surface superior to 200 m² were
missing. The improvement step allows to reconstruct
73 % of the omissions and 70 % of the tile coating
buildings (Figure 15).
Urban Indicator for Database Updating - A Decision Tool to Help Stakeholders and Map Producers
87
Figure 15: Improvement due to elevation and NDVI data.
The major inconvenient to this step is that it adds
commission errors to the results. Indeed, Figure 15
shows the reconstruction of 33 % of destroyed
buildings.
8 CONCLUSION
This semi-automatic tool provides detections of
rapidly evolving features that does not match any
evolution scheme: urban areas. It tends to answer the
problem of efficiency in the updating databases
process answering the questions “Where? How
much? When?”. The results can be presented in a
map, to spot the necessity of the database update, it
can be presented over the original image to help the
producers to focus on important updating area
(especially destroyed buildings), or it can be used at
the end of the production process as a quality
control. The method, using radiometric primitives
improvable with geometric primitives, is adaptable
to the type of landscapes, images, scales, and has
been recognized as useful by independent producers
during a real updating process test, not only as a
detection of anthropogenic areas process, but as a
real decision support instrument.
9 DISCUSSION
If this tool has proved its efficiency in a context of
rapid mapping, it cannot be trusted as it is for an
exhaustive automatic mapping. Indeed, a building
that has changed its shape between the date of the
database and the date of the image will probably be
reconstructed as it was in the past.
If we were able to observe very few errors of
commission (error type I) we didn’t establish a real
statistics on a representative amount of elements.
Moreover, in the statistics established on type II
errors, some missing polygons were considered as
omission but the field reality (the image) was
obviously different than the representing database:
in this case the qualification of omission is not really
correct.
In this study, two types of databases were
tested. The most advanced case (Dire Dawa) used a
database in which the buildings were individually
drawn. This allowed us doing the reconstruction and
the derived statistics and declaring the method
efficient. This is not possible with a database in
which the buildings are not individually drawn. This
is why a simple visual qualification is done on the
other tested areas (§ 6.2). When using this type of
database another type of statistics can be calculated
to estimate the changes and the amount of work in
the pre-production phase, but the quality assessment
of the method by calculating the reconstruction rate
is not possible. Another option would be to compare
our detection to other urban products (mixing
different types of data) like Global Urban Footprint
or Landscan.
REFERENCES
ArcGIS for desktop Help http://desktop.arcgis.com/
en/arcmap/latest/tools/data-management-toolbox/
minimum-bounding-geometry.htm
M. Bernard, T. Rousselin, N. Saporiti and M. Chikri, 2007
“Data harmonisation and optimisation for
development of multi-scale vector databases”, ISPRS
Workshop on Updating Geo-Spatial Databases with
Imagery, Urumqi.
N. Champion, 2011 “Détection de changement 2D à
partir d’imagerie satellitaire. Application à la mise à
jour des bases de données géographiques ”, thesis
ENVI 4.2, 2005 User’s Guide Volume 2 Chapter 7.
A.A. Green, M. Berman, P. Switzer, and M.D. Craig, 1988
A transformation for ordering multispectral data in
terms of image quality with implications for noise
removal”. IEEE Transactions on Geoscience and
Remote Sensing (GRS), Volume 26 No 1 p.65-74.
R. Haralick, K. Shanmugan and I. Dinstein, 1973
“Textural features for image classification”, IEEE
Transactions on systems, man and cybernetics Vol.
SMC-3, N°6, pp.610-621
D. Lu et al., 2003 “Change Detection Techniques”,
International Journal of Remote Sensing vol.25, no 12,
2365-2407.
D. Lu and Q. Weng, 2004 “Spectral mixture analysis of
the urban landscapes in Indianapolis with Landsat
ETM+ imagery”, Photogrammetric Engineering and
Remote Sensing, 69, 973-980.
D. Lu and Q. Weng, 2006 “Use of impervious surface in
urban land-use classification”, Remote Sensing of
Environment 102 (1), 146-160
M. Van Droogenbroeck, 2015-2016, “Computer vision”,
open Repository and Bibliography, Université de
Liège.
S. Vermillion and S. Sader, 1999, Use of the Minimum
Noise Fraction (MNF) Transform to Analyze Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) Data
GISTAM 2017 - 3rd International Conference on Geographical Information Systems Theory, Applications and Management
88
of Northern Forest Types, AVIRIS workshop
bibliography
V. Walter, 2000 “Automatic change detection in GIS
databases based on classification of multispectral-
data”, ISPRS Archive Vol. XXXIII Part B4,
Amsterdam.
Q. Zhang and J. Wang , 2003 “A rule-based urban land
use inferring method for fine-resolution multispectral
imagery”, Canadian Journal of Remote Sensing, 29, 1-
13
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