UNCORRECTED
PROOF
12
Assessment of the state of conservation of buildings through roof
3
mapping using very high spatial resolution images
4
Luísa Gonçalves
a,*, Cidália
C.
Fonte
b,c, Eduardo
N.B.S.
Júlio
d, Mario Caetano
e,f5 a
Polytechnic Institute of Leiria, School of Technology and Management,Morro do Lena – Alto Vieiro, Apart. 4163, 2411-901 Leiria,Portugal 6 bInstitute for Systems and Computers Engineering at Coimbra, Portugal
7 cDepartment of Mathematics, University of Coimbra, Portugal
8 d
ISISE, Civil Engineering Department, University of Coimbra, Portugal
9 e
Portuguese Geographic Institute (IGP), Remote Sensing Unit (RSU), Lisboa, Portugal
10 f
CEGI, Instituto Superior de Estatística e Gestão de Informação, ISEGI, Universidade Nova de Lisboa, Portugal
Q1 11 1 3
a r t i c l e
i n f o
14 Article history: 15 Received 4 September 2008 16 Accepted 11 March 2009 17 Available online xxxx 18 Keywords: 19 Building roofs 20 Soft classification 21 Hybrid classification 22 Measures of uncertainty 23 Cultural heritage 24 2 5a b s t r a c t
26 The assessment of the state of conservation of buildings is extremely important in urban rehabilitation. In27 the case of historical towns or city centres, the pathological characterization using traditional methods is
28 a laborious and time consuming procedure. This study aims to show that Very High Spatial Resolution
29 (VHSR) multispectral images can be used to obtain information regarding the state of conservation of
30 roofs where, usually, building degradation starts. The study was performed with multispectral aerial
31 images with a spatial resolution of 0.5 m. To extract the required information, a hybrid classification
32 method was developed, that integrates pixel and object based classification methods, as well as
informa-33 tion regarding the classification uncertainty. The proposed method was tested on the classification of the
34 historical city centre of Coimbra, in Portugal, that includes over than 800 buildings. The results were then
35 validated with the data obtained from a study conducted during2years by a nine element team from the
36 University of Coimbra, using traditional methods. The study performed achieved a global classification
37 accuracy of 78%, which proves that the state of conservation of roofs can be obtained from VHSR
multi-38 spectral images using the described methodology with a fairly good accuracy.
39 Ó 2009 Published by Elsevier Ltd.
40 41
42 1. Introduction
43 Before planning an urban intervention in historical towns or
44 city centres, it is necessary to characterize what exists by
perform-45 ing an architectural and structural survey of buildings within that
46 area[10]. This plays a crucial role in defining the required
conser-47 vation or rehabilitation operations. Traditional techniques used to
48 identify and map both structural and non-structural anomalies are
49 manually performed and require an individual on-site analysis of
50 each building. These techniques present major drawbacks as they
51 are work-intensive and time consuming [10]. Furthermore,
fre-52 quently some parts of the buildings such as roofs are inaccessible,
53 which makes the inspection process even more difficult. Therefore,
54 it is necessary to develop new methods, by exploring other
possi-55 ble sources of information, that can provide data on existing
anom-56 alies, such as durt, deterioration, cracking, biological colonization,
57 moss, and pioneer vegetation, overcoming the disadvantages of
58 traditional methods previously referred to.
59
Some experimental tests, preformed mainly on stone
monu-60
ments and masonry walls, proved that it is possible to obtain
accu-61
rate information about materials and degradations on buildings
62
facades through an automatic classification from visible and near
63
infrared photographs (e.g.[10,12,13,15]). The study reported here
64
intends to evaluate the applicability of Very High Spatial
Resolu-65
tion (VHSR) multispectral aerial images, which have additional
66
problems due to geometric and atmospheric distortions when
67
compared to ground photographs, to detect damages on more than
68
800 building roofs situated in a historical city centre and to identify
69
their state of conservation. Multispectral aerial images, with a
spa-70
tial resolution of 0.5 m in four spectral wavebands, which has
spa-71
tial and spectral characteristics comparable to VHSR satellite
72
images, were used.
73
Although the VHSR multispectral images have great potential,
74
they also present drawbacks and limitations, such as, the increase
75
of the spectral variability and the amount of shadows, as well as
76
the enormous amounts of data[3]. In addition, although the
num-77
ber of pure pixels increases with the spatial resolution, this
in-78
crease is followed by the presence of classes that are mosaics of
79
single entities or a spatial arrangement of pixels, such as buildings.
80
There are several available techniques for the automatic extraction
0950-0618/$ - see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.conbuildmat.2009.03.002
* Corresponding author. Tel.: +351 244 843 339; fax: +351 244 820 310. E-mail address:luisag@estg.ipleiria.pt(L. Gonçalves).
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Construction and Building Materials
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PROOF
81 of information from these images. They can be performed on apix-82 el basis(e.g.[17,2,19,11]) object basis(e.g.[8,3]) or a combination
83 of both(e.g. [18,14]). This last approach has been named as
per-84 field classification when the objects are obtained from digital
vec-85 tor data or Geographic Information Systems (GIS)[1]. Even though,
86 some new approaches have been developed, the efficient
extrac-87 tion of information from VHSR images still remains to impose
88 new demands and the need to develop methodologies that
incor-89 porate shape and context, which are some of the main clues used
90 by a human interpreter, and a closer integration of remote sensing
91 and GIS[4,3].
92 In this study a hybrid classification method is developed to
ob-93 tain information about the buildings roof materials and their
94 pathologies. The method combines pixel and object classification
95 and incorporates uncertainty information in the soft automatic
96 classification of the images. This approach has similarities with
97 the per-field classification since the objects used in the
classifica-98 tion process were obtained from a vector cartographic map of
99 the region. The results are then compared with the ones obtained
100 with a traditional pathology survey, conducted during2years by
101 a nine element team from the University of Coimbra, and some
102 conclusions are drawn.
103 2. Data set and case-study area
104 The case-study area selected is the historical city centre of
105 Coimbra, in Portugal, with an area of approximately 14.8 ha, that
106 includes over than 800 buildings, some of them are historical
107 buildings, colleges and monasteries, most of these from the 16th
108 century, innumerable alleyways and small squares (Fig. 1).
109 In the study herein described, we used multispectral aerial
110 images with a resolution of 0.5 m, which belongs to a set ofortho
111 rectifieddigital images covering the Portuguese mainland territory,
112 obtained by plane between 2005 and 2006 with anUltraCamTM sen-113 sor of Vexcel by the Portuguese Geographic Institute (IGP) and the
114Q2 Portuguese Forest Services(DGRF).The study was performed using
115 four multispectral bands (blue, green, red and near infrared) and
116 the pixels are recorded ineightbits. A vegetation index was also
117 computed for each data image, namely the Normalized Difference
118 Vegetation Index (NDVI, Eq. (1)) which was used as additional
119 band information to improve the detection of areas with
chloro-120 phyll (e.g. moss, lichen, moulds).
121
NDVI ¼ ð
q
nirq
redÞ=ðq
nirþq
redÞ ð1Þ123 123
124
where
q
nirandq
redrepresent the surface reflectance ofnearinfra-125
redand redbands,respectively.
126
A vector cartographic map, at the 1:1000 scale, was used to
ob-127
tain the polygons corresponding to the buildings of the case-study
128
area (Fig. 1). The buildings information was extracted from the
car-129
tography and imported to a GIS.
130
3. Proposed method
131
Multispectral classification methods are based on the fact that
132
the reflectance characteristics of the different land cover classes
133
depend upon the radiation wavelength. The representation of the
134
intensity values registered at each pixel for n image channels
(cor-135
responding to n different wavelengths) into ann-dimensional
fea-136
ture space generates a cluster, in thisn-dimensionalspace, for each
137
class. In the supervised classification methods, reference areas are
138
used to identify and mark this clusters.
139
In this study, a previous analysis, by visual interpretation, of the
140
images were made to evaluate if it was possible to identify the
141
main materials existing in the area, the presence of pathologies
142
in the buildings roofs and, when present, the different kind of
143
pathologies. It was possible to conclude that, even though the
144
images have very high spatial resolution, these only enable the
145
identification of the roofs with pathologies and do not allow the
146
identification of their different types. Besides that, the majority
147
of roofs have several types of pathologies, which makes the
defini-148
tion of the reference (or training) areas, which are used to perform
149
the classification and validation, considerably difficult, even with
150
0.5 m pixel spatial resolution. This previous analysis was followed
151
by the definition of the method, the definition of classes and the
152
identification of reference areas.
153
The main roof materials in the case-study area are: ceramic
154
tiles; fibrocement corrugated sheets; and steel panels. The results
155
obtained by the survey performed by the University of Coimbra
re-156
vealed that, from the 681 buildings studied (82% of the buildings of
157
the case-study area), 90% have ceramic tile roof materials. For this
158
reason, the present study focuses only in the identification of
159
buildings with ceramic tile roof pathologies, to obtain a Building
160
Pathology Map (BPM). The extraction of this kind of information
161
from the image requires, not only the classification of the
elemen-162
tary entities (pixels in this case), but also the analysis of their
dis-163
tribution inside the buildings. To achieve this goal, an independent
164
identification of the roof materials, such as ceramic roof tile and
165
steel roof panel, was made, producing the information about the
Fig. 1. The data of the case-study area: (a) aerial images (RGB321); (b) overlay of the vector buildings map and the aerial images.
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PROOF
166 Surface Elements Material (SEM). The same approach was used to167 identify the parts of buildings that present any kind of pathology,
168 producing the information about the Surface Elements Pathology
169 (SEP). The classification method developed is a hybrid approach
170 that includes two preliminary pixel classifications of the image,
171 constructing a Surface Elements Materials Map (SEMM) and a
Sur-172 face Elements Pathology Map (SEPM), based on a soft probabilistic
173 pixel classification. This classification method assigns, to each
pix-174 el, different degrees of probability to the several classes under
con-175 sideration. This extra data provided additional information at the
176 pixel level which allowed the assessment of the classification
177 uncertainty.
178 Since the survey used as reference information produced a
clas-179 sification of the buildings conservation level, to allow the
compar-180 ison of the results obtained with both approaches, it was crucial
181 that the results produced with this new approach would also be
182 at the building level. Therefore, the subsequent step was the
iden-183 tification of the Building Units (BU), which was obtained from the
184 vector cartographic map available at 1:1000 scale. This information
185 was converted to the raster format, generating a raster buildings
186 map, and was used as a mask, so that only the parts of the classified
187 image inside the buildings were processed to obtain the BPM. At
188 this stage, the BU becomes the basic units rather than the
individ-189 ual pixels. The pathologies identification within each BU was
per-190 formed considering a set of rules that included information about:
191 (1)the distribution of the classified pixels;(2)the probabilities
as-192 signed to the several classes for each pixel;(3)the degree of
uncer-193 tainty associated to these assignments. The introduction of
194 uncertainty information, associated to the pixel classification of
195 SEP and the SEM, was used to avoid the use of misclassified pixels
196
in the transformation from the SEMM and the SEPM to the BPM,
197
since it was shown that this approach improves the land cover
198
classification[9].
199
The proposed method includes the following steps (seeFig. 2):
200
(1)softpixel basedclassification of the multispectral aerial image
201
to obtain the SEMM and the SEPM;(2)evaluation of the
classifica-202
tion;(3)evaluation of the classification uncertainty;(4)pathology
203
classification of the buildings with ceramic tile material, based on
204
decision rules using the SEMM the SEPM and their uncertainty
205
information;(5)evaluation of the classification accuracy.
206
3.1. Training and testing data
207
The sampling design has an important role in the classification
208
and accuracy assessment. In this study five sampling protocols
209
were established. Two for training, respectively, the SEP and the
210
SEM pixel classification, two to evaluate these classifications and
211
another to evaluate the final building map classification. The
train-212
ing dataset consisted on a semi-random selection of sites (Fig. 3).
213
For each class, fifteen building polygons were selected manually
214
from the case-study area and a stratified random selection of 100
215
samples inside the chosen buildings was performed. To evaluate
216
the soft pixel classification accuracy a stratified random sample
217
of 100 pixels per class was made, spread over all buildings. This
218
number of pixels was used because it is recommended to obtain
219
a standard error of 0.05 for the estimated user’s accuracy of each
220
class[16]. The soft pixel classification was only evaluated inside
221
the building polygons. For the accuracy assessment of the final
222
BPM a stratified random selection of 100 samples was performed
223
considering all buildings. The accuracy assessment was made with
Fig. 2. Flowchart of the method.
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PROOF
224 an error matrix. In this stage, the survey performed by theUniver-225 sity of Coimbra regarding all buildings was used as reference
infor-226 mation to identify the ceramic building roofs with anomalies and
227 not deteriorated in the case-study area.
228 3.2. Spectral analysis
229 The information of the training data set was also used to study
230 the spectral separability of the classes at the pixel level. For this
231 purpose the Bhattacharya Distance (B-Distance) and a dimensional
232 scatter plot were used.
233 To visualize the results the red and the near infrared bands were
234 used as axis, since, due to the presence of vegetation and patches in
235 some roofs, these two bands are the ones that better allow the
dif-236 ferentiation between the classes. The B-Distance can serve as an
237 indicator to the final performance of the supervised classification.
238 A high B-Distance value means that classes are spectrally
separa-239 ble, therefore, the larger the B-Distance value the better the final
240 classification.
241 3.3. Classification
242 The classification method includes several steps. First, two soft
243 pixel classifications of the image were performed using a
probabi-244 listic classifier based on Bayesian modelling, for the identification
245 and mapping of the SEP and the SEM. This classifier was selected
246 because it has been used in other studies to derive accurate
classi-247 fications (e.g. [2,19,5]). The Bayesian classifier is similar to the
248 Maximum Likelihood Classifier (MLC), the most widely used image
249 classifier in remote sensing, but it has been mainly used in its crisp
250 version. However, the output may be derived in the form of
poster-251 ior class probabilities providing a soft classification(e.g.[17,6,7]).
252 Unlike traditional hard classifiers, the output is not a single
classi-253 fied map, but rather a set of images (one per class) that express the
254 probability that each pixel belongs to the class in question. This
ex-255 tra data also provided additional information at the pixel level
256 which allowed the assessment of the classification uncertainty.
257 The soft pixel classification of the SEM was driven using four
mul-258 tispectral bands and the SEP was performed also with the NDVI
259 vegetation index.
260 The surface elements classes used in this study for the SEMM
261 were ‘‘Dark Ceramic Roof Tile” (CT-D), ‘‘Bright Ceramic Roof Tile”
262 (CT-B) ‘‘Fibrocement Corrugated Roof Sheet” (FC), ‘‘Steel Roof
263 Panel” (SP) and ‘‘Roof Shadow” (RS). For the SEPM the classes were
264 ‘‘Ceramic Roof Tile Anomalies” (C-A), ‘‘Ceramic Roof Tile Not
Dete-265 riorated” (C-ND) and Ceramic Roof tile with Shadow (C-S). The Roof
266 Shadow and Ceramic Roof tile with Shadow classes were
consid-267
ered due to the significant amount of shadows that the VHSR
mul-268
tispectral aerial images presented. By considering these classes in
269
the development of classification rules it was possible to reduce
270
misclassification of roof materials and pathologies at the building
271
level.
272
Secondly, the uncertainty of the probabilistic classification was
273
evaluated using an indicator of theClassification Uncertainty(CU),
274
available in the commercial software IDRISIS, given by
CU ¼ 1 max i¼1;...;nðpiÞ Pn i¼1pi n 1 1 n ð2Þ 276 276 277
where pi(i =1, . . . , n)are the probabilities associated to the several
278
classes and n is the number of classes under consideration. This
279
indicator assumes values in the interval [0, 1]and only depends
280
on: the maximum probability; the sum of all probabilities assigned
281
to the class; the total number of classes. CU evaluates until which
282
point the classification is dispersed over more than one class and
283
the degree of compatibility with the most probable class, providing
284
information regarding the classifier difficulty in assigning only one
285
class to each pixel.
286
The subsequent step was the construction of the BPM through
287
the combination of the SEPM, the SEMM, their uncertainty
infor-288
mation and the buildings polygons converted to the raster format
289
and used as a mask. This was achieved through a development of
290
rules that incorporate the arrangement of the previous soft pixel
291
based classifications within each BU, the information on the
prob-292
abilities assigned to the several classes at each pixel and the degree
293
of uncertainty associated to these assignments. The rules
construc-294
tion requires a preliminary analysis of the probabilities assigned to
295
the SEP and SEM classes and their uncertainty in order to choose
296
the appropriate thresholds. The BU classes used in this study are:
297
‘‘Buildings with Ceramic Roof Tile With Anomalies”(B-C-A), varying
298
from ‘‘State of Conservation 1” (SC1) to ‘‘State of Conservation 4”
299
(SC4); and ‘‘Buildings With Ceramic Roof Tile Not Deteriorated”
300
(B-C-ND), corresponding to ‘‘State of Conservation 5” (SC5).
301
The transformation of a SEPM and a SEMM into a BPM is similar
302
to a decision tree which, for geographical objects, is a hierarchical
303
structure consisting of several levels. At each level a test is applied
304
to one or more attribute values. Application of the rule results
305
either into a leaf, allocating an object to a class, or a new decision
306
node, specifying a further decision rule.Fig. 4shows the BU classes
307
classification workflow andTable 1shows the used rules. The aim
308
of rules1–3is to make a distinction between ‘Buildings With
Cera-309
mic Roof Tile’ C) and ‘Buildings With No Ceramic Roof Tile’
(B-310
NC). Rule4–6classifies the ‘Buildings With Ceramic Roof Tile’ into
311
‘‘Buildings With Ceramic Roof Tile With Anomalies” and ‘‘Buildings
312
With Ceramic Roof Tile Not Deteriorated”, corresponding to ‘‘State
313
of Conservation 5”. Rule 7 assigns the ‘‘Buildings With Ceramic
314
Roof Tile With Anomalies” (B-C-A) to one of four possible classes:
315
‘‘State of Conservation 1”, ‘‘State of Conservation 2”, ‘‘State of
Con-316
servation 3” and ‘‘State of Conservation 4”.
317
4. Results and discussion
318
4.1. Spectral analysis
319
Figs. 5 and 6show the spectral separability of, respectively, the
320
SEM and SEP classes. It can be observed that, for the represented
321
wavelengths, the ellipses corresponding to all SEM and SEP classes
322
are almost completely separated. Only some minor overlap is
ob-323
served, mainly between Roof Shadows (RS) and the classes
Fibroce-324
ment Corrugated Roof Sheet (FC) and Dark Ceramic Roof Tile
(CT-325
D). Ceramic Roof Tile with Shadows (C-S) spectral signature also
326
presents a slight overlap with the class Ceramic Roof Tile with
Fig. 3. Example of sample training areas in the aerial images RGB321.
UNCORRECTED
PROOF
327 Anomalies (C-A). The results given by theB-Distancereinforce the328 results shown by the scatter plot. The average separability measure
329 of the SEMM classes is 1.87 and for the SEPM the average
separa-330 bility is 1.93 indicating a good performance of the supervised
331 classification.
332 4.2. Classification
333 The accuracy of the SEMM and SEPM produced is presented in
334 Table 2, expressed by the Global Accuracy (GA) and the Kappa
coef-335
ficient. The accuracies of the SEMM and the SEPM obtained are very
336
similar, but the SEPM one is slightly better, which is consistent with
337
theB-Distanceresults. The User’s and Producer’s accuracy of the
338
SEMM and SEPM arepresented, respectively,inFigs. 7 and 8.
339
The User´s and Producer’s accuracy values per class show that
340
the class Ceramic Roof Tile (CT) was very well identified. Some
con-341
fusion was observed between Fibrocement Corrugated Roof Sheet
342
(FC), Steel Roof Panel (SP) and Roof Shadow (RS), which lowered
343
the respective accuracy specific indices. This confusion was due
344
to the proximity of the corresponding spectral signatures.
Fig. 4. Building unit classes classification workflow.
Table 1
Classification rules. Rule
Number
Rules If true
1 At least 50% of the pixels of the SEMM, located inside the BU, are classified as ‘‘Ceramic Roof Tile” with a probability higher than 0.75 and uncertainty less than 0.25.
Assign to class B-C
2 If the percentage of ‘‘Ceramic Roof Tile”, with a probability higher than 0.75 and uncertainty less than 0.25, inside the BU, is higher than the percentage of ‘‘Fibrocement Corrugated Roof Sheet” and ‘‘Steel Roof Panel” and higher than the percentage of ‘‘Roof Shadow”.
Assign to class B-C
3 If the percentage of ‘‘Ceramic Roof Tile”, with a probability higher than 0.75 and uncertainty less than 0.25, inside the BU, is higher than the percentage of ‘‘Fibrocement Corrugated Roof Sheet” and ‘‘Steel Roof Panel” and the percentage of ‘‘Roof Shadow is higher than 0,5.
Assign to class B-C
4 At least 50% of the pixels of the SEPM inside the BU classified as ‘‘Buildings With Ceramic Roof Tile”, is ‘‘ Ceramic Roof Tile With Anomalies” with a probability higher than 0.75 and uncertainty less than 0.25.
Assign to class B-C-A
5 If the percentage of ‘‘ Ceramic Roof Tile With Anomalies” inside the BU classified as ‘‘Buildings With Ceramic Roof Tile” is higher than the percentage of ‘‘Ceramic Roof Tile Not Deteriorated” and higher than the percentage of ‘‘Roof Shadow”.
Assign to class B-C-A
6 If the percentage of ‘‘ Ceramic Roof Tile With Anomalies” inside the BU classified as ‘‘Buildings With Ceramic Roof Tile” is higher than the percentage of ‘‘Ceramic Roof Tile Not Deteriorated” and the percentage of ‘‘Roof Shadow” is higher than 0.5.
Assign to class B-C-A
7 Buildings classified as ‘‘Buildings with Ceramic Roof Tile With Anomalies” have more than 75% of pixels classified as ‘‘Ceramic Roof Tile With Anomalies” with a probability higher than 0.75 and uncertainty lower than 0.25.
SC1
Buildings classified as ‘‘Buildings with Ceramic Roof Tile With Anomalies” have between 50% and 75% of pixels classified as ‘‘Ceramic Roof Tile with Anomalies” with a probability higher than 0.75 and uncertainty lower than 0.25.
SC2
Buildings classified as ‘‘Buildings with Ceramic Roof Tile With Anomalies” have between 25% and 50% of pixels classified as ‘‘Ceramic Roof Tile With Anomalies” with a probability higher than 0.75 and uncertainty lower than 0.25.
SC3
Buildings classified as ‘‘Buildings with Ceramic Roof Tile With Anomalies” have between 0% and 25% of pixels classified as ‘‘Ceramic Roof Tile With Anomalies” with a probability higher than 0.75 and uncertainty lower than 0.25.
SC4
UNCORRECTED
PROOF
345 Fig. 8shows that the classification of Ceramic Roof Tile With
346 Anomalies (C-A) presents higher values for the Producer’s accuracy
Fig. 5. Spectral separability of the Surface Elements Materials classes: Dark Ceramic Roof Tile (CT-D); Bright Ceramic Roof Tile (CT-B); Fibrocement Corrugated Roof Sheet” (FC); Steel Roof Panel (SP) and Roof Shadow (RS).
Fig. 6. Spectral separability of the Ceramic Roof Tile with Anomalies (C-A), Not Deteriorated (C-ND) and Ceramic Roof Tile with Shadows (C-S).
Table 2
Classification accuracy indexes.
GA (%) Kappa (%) SEMM 89 86 SEPM 94 92 0 10 20 30 40 50 60 70 80 90 100 CT FC SP RS Classes %
User's accuracy Producer´s accuracy
Fig. 7. User’s and Producer’s accuracy of the Surface Elements Material Map (SEMM) classes: Ceramic Roof Tile (CT), Fibrocement Corrugated Roof Sheet (FC), Steel Roof Panel (SP) and Roof Shadow (RS).
0 10 20 30 40 50 60 70 80 90 100 C-A C-ND C-S Classes %
User's accuracy Producer's accuracy
Fig. 8. User’s and Producer’s accuracy of the Surface Elements Pathology Map (SEPM) classes: Ceramic Roof Tile with Anomalies (C-A), Not Deteriorated (C-ND) and Ceramic Roof Tile with Shadows (C-S).
0 0.04 0.08 0.12 0.16 0.2 CT FC SP RS Classes Uncertainty
Fig. 9. Average classification uncertainty per class of the surface elements material map. 0 0.01 0.02 0.03 0.04 0.05 0.06 C-A C-ND C_S
Classes
Uncertainty
Fig. 10. Average classification uncertainty per class of the surface elements pathology map. 0 10 20 30 40 50 60 70 80 90 100 D N -C -B A -C -B Classes % User's Producer's
Fig. 11. The User’s and Producer’s accuracy of the BPM obtained with the hybrid approach applied to the aerial images.
UNCORRECTED
PROOF
347 than for the User’s accuracy while the opposite occurs for theCera-348 mic Roof Tile Not Deteriorated (C-ND), which means that the C-A
349 class presents more commission errors and the C-ND presents
350 more omission errors.
351 Figs. 9 and 10 show the average uncertainty per class of the
352 SEMM and SEPM, respectively. The comparison of these results
353 with Figs. 7 and 8show that they are consistent. For example,
354 the classes Ceramic Roof Tile (CT) and Steel Roof Panel (RS) in
355 the SEMM present the lower values of uncertainty and the higher
356 values of accuracy, while the other two classes show higher
uncer-357 tainty and lower accuracy.
358 The global classification accuracy of the BPM was 78%. The
359 User’s and Producer’s accuracy are shown inFig. 11. Since the class
360 Buildings With Ceramic Roof Tile With Anomalies (B-C-A) presents
361 lower User’s Accuracy it has more commission errors while the
362 class Buildings With Ceramic Roof Tile Not Deteriorated (B-C-ND)
363 has more omission errors (corresponding to lower Producer’s
Accu-364 racy). This means that some buildings, which were classified with
365 this approach as having anomalies, according to the reference
366 study do not present them.
367 The reference study analyzed 826 buildings within the
case-368 study area. With this traditional survey, 75% of the buildings were
369 identified as having ceramic roof tile while, with the new approach,
370 76% of the buildings were identified to have this type of roof,
cor-371 responding to a slight increase in the identification of this roof
372 material. The comparison between the two methods showed that
373 85% of the buildings with ceramic roof tile identified by the
tradi-374
tional survey were also identified with this new approach. In
addi-375
tion, the comparison between the BPM and the map made with the
376
traditional method showed that 77% of the ceramic roof tile with
377
pathologies, identified with the traditional survey was also
identi-378
fied by this new approach (Fig. 12).
379
The analysis of the differences between the pathology map
ob-380
tained with the traditional methods and BPM leads to the
conclu-381
sion that they are mainly due to the presence of shadows in the
382
image, to the fact that the image and the vector cartographic
383
map do not match perfectly (as shown inFig. 1) and mainly
be-384
cause the traditional survey was made between 2003 and 2004
385
and the aerial images were produced from aerial digital photos
ob-386
tained between 2005 and 2006.
387
Since the shadows spectral signature presents some overlap
388
with the class Ceramic Roof Tile With Anomalies (C-A), some
build-389
ings with a great amount of shadows may be wrongly classified as
390
presenting anomalies, which may explain the amount of
commis-391
sion and omission errors shown in the B-C-A and B-C-ND classes
392
(seeFig. 11). The mismatches between the buildings in the vector
393
cartographic map and the image have the consequence that some
394
pixels placed inside the regions corresponding to the buildings
395
actually do not represent parts of buildings and therefore may
gen-396
erate wrong conclusions. Since there is a temporal discrepancy
be-397
tween the data used in both studies some changes occurred in the
398
region, and therefore the different results obtained with both
stud-399
ies for some building may actually be both correct. For example,
400
between these two periods several roofs were repaired or replaced
Fig. 12. Comparison between the Building Pathology Map (BPM) and the map made with the traditional approach: (a) Pathology map generated with traditional methods; (b) BPM; (c) difference between the two maps.
Fig. 13. (a) Extract of the aerial images (RGB321) overlaid with the buildings with ceramic roof tile not deteriorated obtained with the traditional methods. (b) Extract of the aerial images (RGB321) overlaid with the buildings with ceramic roof tile not deteriorated classified by the new approach. The circles mark some of the buildings which roofs were repaired or replaced by new ones and were correctly identified with the new classification approach.
UNCORRECTED
PROOF
401 with new ones, which therefore did not present any anomaly in the402 aerial images and were well identified with the new classification
403 approach (seeFig. 13). On the other hand some roofs that did not
404 present anomalies in 2003–2004 may have developed some since
405 then, which became visible in 2005–2006.
406 The increase in VHSR multispectral images spatial resolution,
407 despite enabling the identification of ceramic tile material also
al-408 lows the identification of other features, such as regions with
cera-409 mic tiles that belong to the same roof but present different
410 brightness or shadows due to slope differences and sun
orienta-411 tion. This aspect is a major problem for this kind of application.
412 However, the proposed hybrid method of classification proved to
413 be adequate to solve this kind of difficulties, since it enabled a
cor-414 rect transformation of surface elements into building classification
415 units (seeFig. 14).
416 5. Conclusions
417 The obtained results show that the application of very high
spa-418 tial resolution (VHSR) multispectral images to heritage
conserva-419 tion, in particular to the extraction of information concerning
420 roof anomalies, is very promising. The VHSR image used in the
421 study, enabled a good identification of the different roof materials
422 and the presence of their damages. The use of a hybrid pixel-object
423 classification method integrating the surface elements
classifica-424 tion uncertainty proved to be valuable and adequate in the
classi-425 fication process to resolve some difficulties found along the study.
426 The global classification accuracy of the Building Pathology Map
427 (BPM) was 78%, and therefore further attention should be given
428 to this approach. From a methodological point of view, the hybrid
429 approach also proved to be adequate for the transformation of
sur-430 face elements into BPM with a format well suited to be integrated
431 in a GIS.
432 Acknowledgements
433 The authors thank the University of Coimbra for providing data
434 related to the state of conservation of buildings in the historical
435 city centre of Coimbra.
436 References
437 [1] Aplin P, Atkinson P, Curran P. Per-field classification of land use using the 438 forthcoming very fine spatial resolution satellite sensors: problems and
439 potential solutions. In: Atkinson P, Tate N, editors. Advances in remote
440 sensing and GIS analysis. Chichester: John Wiley and Sons; 1999. p. 219–39.
441 [2] Atkinson PM, Cutler MEJ, Lewis H. Mapping sub-pixel proportional land cover
442 with AVHRR imagery. Int J Rem Sens 1997;18(4):917–35.
443 [3] Blaschke T, Burnett C, Pekkarinen A. Image segmentation methods for
object-444 based analysis and classification. In: Jong SM, van der Meer FD, editors. Remote
445 sensing image analysis; 2004. p. 211–34.
446 [4] Donnay JP, Barnsley M, Longley P. Remote sensing and urban analysis. In:
447 Donnay JP, Barnsley M, Longley P, editors. Remote sensing and urban
448 analysis. New York: Taylor & Francis; 2001. p. 3–18.
449 [5] Eastman JR, Laney RM. Bayesian soft classification for sub-pixel analysis: a
450 critical evaluation. Photogramm Eng Rem Sens 2002;68:1149–54.
451 [6] Foody GM. Approaches for the production and evaluation of fuzzy land cover
452 classifications from remotely-sensed data. Int J Rem Sens 1996;17:1317–40.
453 [7] Foody GM. Sub-pixel methods in remote sensing. In: Jong SM, van der Meer FD,
454 editors. Remote sensing image analysis: including the spatial domain; 2004. p.
455 37–49.
456 [8] Gonçalves L, Caetano M. Classificação das imagens do satélite IKONOS
457 utilizando uma abordagem orientada por objectos. In: Bastos L, Matos J, Lidel
458 L, editors. Acta da III Conferência de Cartografia e Geodesia; 2004. p. 287–98.
459 [9] Gonçalves L, Fonte C, Júlio E, Caetano M. A method to incorporate uncertainty
460 in the classification of remote sensing images. In: Eighth international
461 symposium on spatial accuracy assessment in natural resources and
462 environmental sciences, Shanghai, China; June 2008.
463 [10] Hemmleb M, Weritz F, Maierhofer C. Damage detection on buildings surfaces
464 with multi-spectral techniques. In: Proc XX CIPA international symposium.
465 Torino: Italy; 2005.
466 [11] Ibrahim MA, Arora MK, Ghosh SK. Estimating and accommodating uncertainty
467 through the soft classification of remote sensing data. Int J Rem Sens
468 2005;26:2995–3007.
469 [12] Lerma JL. Automatic feature recognition technique on stone monuments using
470 visible and IR photography. In: Proc int cultural heritage informatics meeting,
471 vol. 2; 2001. p. 255–8.
472 [13] Lerma JL. Automatic plotting of architectural facades with multispectral
473 images. J Surv Eng 2005;131(3):77.
474 [14] Plantier T, Caetano M. Mapas do Coberto Florestal: Abordagem Combinada
475 Pixel/Objecto. In: Bastos L, Matos J, Lidel L, editors. Acta da V Conferência
476 Nacional de Cartografia e Geodesia; 2007. p. 157–66.
477 [15] Ruiz LA, Lerma JL, Gimeno J. Application of computer vision techniques to
478 suport in the restoration of hitorical buildings. International archives of
479 photogrammetry and remote sensing. Comission III, vol. XXXIV, Part 3A+B;
480 2002. p. 4.
481 [16] Stehman SV. Statistical rigor and practical utility in thematic map accuracy
482 assessment. Photogramm Eng Rem Sens 2001;67(6):727–34.
483 [17] Wang F. Improving remote sensing image analysis though fuzzy information
484 representation. Photogramm Eng Rem Sens 1990;56:1163–9.
485 [18] Wang L, Sousa WP, Gong P. Integration of object-based and pixel-based
486 classification for mapping mangroves with IKONOS imagery. Int J Rem Sens
487 2004;20(24):5655–68.
488 [19] Zhang J, Foody GM. Fully-fuzzy supervised classification of sub-urban land
489 cover from remotely sensed imagery: statistical and artificial neural network
490 approaches. Int J Rem Sens 2001;22:615–28.
491 Fig. 14. Transformation of surface elements into building classification units: (a) Extract of the aerial images RGB321; (b) Surface elements pixel classification into ‘‘Dark Ceramic Roof Tile” (CT-D), ‘‘Bright Ceramic Roof Tile” (CT-B) and Roof Shadow (RS); (c) Building units classification into ‘‘Ceramic Tile” (CT) and ‘‘Fibrocement Corrugated Roof Sheet or Steel Roof Panel” (FC+SP). The circles mark a building which presented shadows, and bright and dark ceramic roof tile and were correctly identified with this new classification approach.
