Journal of Building Engineering 44 (2021) 102603
Contents lists available at ScienceDirect
Journal of Building Engineering
journal homepage: www.elsevier.com/locate/jobe
Characterizing building materials using multispectral imagery and LiDAR
intensity data
Zohreh Zahiri a, Debra F. Laefer b, *, Aoife Gowen c
a
Department of Physics University of Antwerp, Universiteitsplein 1, B-2610, Antwerpen, Belgium
Center for Urban Science and Progress and Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 370 Jay St., Brooklyn, NY,
11201, USA
c
UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Multispectral
Laser scanning
Building materials
Classification
Façades
This paper addresses the underlying bottleneck of unknown materials and material characteristics for assessing
the life cycle of an existing structure or considering interventions. This is done by classifying and characterizing
common building materials with two readily accessible, remote sensing technologies: multispectral imaging and
Light Detection and Ranging (LiDAR). A total of 142 samples, including concrete of 3 different water/cement
ratios, 2 mortar types, and 2 brick types (each type fired at 3 different temperatures) were scanned using a 5band multispectral camera in the visible, RedEdge, and Near Infrared range and 2 laser scanners with
different wavelengths. A Partial Least Squares Discriminant Analysis model was developed to classify the main
materials and then the subcategories within each material type. With multispectral data, an 82.75% average
correct classification rate was achieved (improving to 83.07% when combined with LiDAR intensity data), but
the effect was not uniformly positive. This paper demonstrates the potential to identify building materials in a
non-destructive, non-contact manner to aid in in-situ facade material labelling.
1. Introduction
Presently, obtaining fast and cheap information about building ge­
ometries and materials at a city-scale plays an important role in many
urban applications including asset management, computational model­
ling, and resiliency assessment. Despite significant research that has
been undertaken for the automatic detection of the geometry of build­
ings (e.g. Refs. [1,2] and individual features or defects [3,4], relatively
little has been done to remotely identify component materials [5,6],
especially characterizing differences within single classes of materials
(e.g. weak stone versus strong stone).
As noted by Dizhur et al. [7]; “One of the primary needs when
assessing a building for refurbishment and/or retrofit is to characterize
the constituent material properties.”. However, doing so without
destructive testing is difficult to achieve, especially at scale [8]. With
respect to asset management knowing such differences can be critical in
terms of long-term performance expectations such as whether a mortar
contains lime [9] or the in-situ strength of concrete [10]. While ultra­
sonic pulse tests have been effective in characterizing different clays and
specific firing levels for structural clay blocks [11], such technology
cannot be deployed at scale. Recent work by Chuta, Colin, and Jeong
[12] on changed surface properties of concretes with different
water-cement ratios provides a rational basis to investigate the use of
non-contact remote sensing for automatic material determination. If
automatic detection of building materials and their differences could be
achieved by remote sensing (RS) data, great improvements could be
obtained for more automated documentation and life-cycle assessment
of existing buildings. This paper considers the possible benefits of two RS
techniques in detecting primary building materials and distinguishing
differences within classes of materials.
2. Background
In recent decades, remote sensing data in different forms [e.g. Light
Detection And Ranging (LiDAR), multispectral imaging, hyperspectral
imaging) have been widely used for classification purposes in many
areas such as geological investigation [13,14], vegetation identification
[15,16], cultural heritage monitoring, and damage detection [17,18].
Such datasets have even been used for estimation of water depth [19]
and quantification of river channel bed morphology [20]. However,
* Corresponding author.
E-mail address: zohreh.zahiri@uantwerpen.be (D.F. Laefer).
Available online 27 April 2021
https://doi.org/10.1016/j.jobe.2021.102603
2352-7102/© 2021 Published by Elsevier Ltd.
Received 8 November 2020; Received in revised form 7 March 2021; Accepted 21 April 2021
Z. Zahiri et al.
relatively little research has been undertaken to classify building ma­
terials and discern differences within a class of construction materials.
Presently, the most common RS data types for material classification
studies are spectral imaging data.
Early applications of the spectral imaging in building material
characterization were conducted through reflected-light imagery in the
visible and near-infrared (NIR) ranges with both unsupervised classifi­
cation [21] and supervised classification approaches [22]. Lerma, Ruiz,
and Buchón [23] combined spectral images [red, green, blue (RGB), and
NIR] with three textural images (calculated from the Red band image)
for classification of six different materials (cleaned cement mortar, dirty
cement mortar, flaking, polished limestone, washed limestone, and dark
limestone) from the façade of Santos Juanés Church in Valencia, Spain
using a maximum likelihood classification approach [23]. While
combining the textural images and multispectral images qualitatively
improved the classification images, no quantitative results were pre­
sented. Later, Lerma [24] investigated the advantages of using
multi-band data in the identification of different facade materials (i.e.,
rock, wood, and various cement mortars). Multi-band images were
generated using a set of registered images related to the same object but
taken at different times, from distinct positions, under various lighting
conditions and with multiple sensors of different electromagnetic
wavelengths (RGB and NIR). In that work, multi-band data combined
with multi-spectral data obtained better classification results compared
to multi-spectral classification under single lighting and atmospheric
conditions. As that work was done on only a single facade with a limited
number of materials tested, a universal solution has yet to be developed
where a repeatable spectral signature is identifiable.
LiDAR is another RS technique. In that technology, a laser beam is
used to collect range measurements from which 3D coordinates (x-y-z)
are established. LiDAR systems usually operate at a monochromatic
wavelength and record the strength of the reflected energy from the
object encountered in this line-of-sight-technology. The reflected energy
is referred to as the intensity and is controlled by material surface
properties and atmospheric factors, as well as equipment specific con­
siderations [25]. Intensity values are the most common LiDAR outputs
used for material classification. At a large scale, this has been success­
fully employed for distinguishing vegetation from non-vegetation [26],
for identifying shadowed areas from non-shadowed ones [27], and for
classifying different urban land covers [25,28].
Sithole [29] combined LiDAR data with hue, saturation, value trip­
lets (obtained from converting RGB values) in a three-dimensional (3D)
triangulation model to detect brick/stone blocks from surrounding
mortar joints. Similarly, Hemmleb et al. [30] deployed multispectral
laser scanners to classify brick, mortar, and stone materials, as well as
damage from moisture, biological agents, and salt blooming, and Morsy,
Shaker, and El-Rabbany [31] combined three intensity images and a
Digital Surface Model image (all created from multispectral LiDAR data)
to classify buildings, trees, roads, and grass. While more versatile than
single-channel laser scanners, multispectral laser scanners are still
limited by their spectral sensitivity and have, therefore, only been able
to distinguish a limited number of materials. More recently, Yuan, Guo,
and Wang [32], used the reflectance, hue saturation values, and surface
roughness extracted from LiDAR data as the material classification
features. They concluded that the reflectance values deduced from
LiDAR intensity values were more accurate than using the intensity
values directly, as the latter varied with respect to the angle of incidence
and distance from the scanner.
In contrast to LiDAR and multispectral technologies, hyperspectral
cameras can collect spectral data from hundreds of continuous bands.
The hyperspectral data in the range of 1300–2200 nm was used recently
to classify concrete with different water/cement ratios [33], bricks of
different clay and firing temperatures [34] and mortars with different
binder types [35]. Although hyperspectral data provides more infor­
mation than regular RGB images and multispectral images, the higher
dimensionality of the data is significantly more complex to process due
to the larger number of readings
[36]. This makes estimation of different
Journal of Building Engineering 44 (2021) 102603
material classes difficult, especially if the training dataset is limited
[37]. Another disadvantage is that hyperspectral cameras are relatively
expensive and heavy, which complicates collecting façade data from
multiple elevations. Conversely, multispectral cameras are cheaper and
lighter, thereby, allowing for a wide range of options for unmanned
aerial vehicle usage, as described by Chen, Laefer, and Mangina. [38];
thereby giving great flexibility in data collection. To date, the combi­
nation of RedEdge multispectral and LiDAR data sets for characterizing
building materials has not been thoroughly explored. This paper con­
tributes to this area.
3. Material and methods
In the study herein, data from a multispectral camera in the Near IR
and RedEdge ranges (RedEdge Micasense) were used to classify multiple
building material samples. The classification process was repeated with
the addition of two LiDAR scans taken with different laser wavelengths,
and various data configurations (i.e. each technique separately or
combined) were tested to optimise classification accuracy, as will be
described in detail in this section.
3.1. Equipment
For the multispectral camera, a RedEdge MicaSense camera was
used. The unit has five sensors each collecting light intensity at five
different bands (Fig. 1): Blue (475 nm), Green (560 nm), Red (668 nm),
NIR (840 nm), and Red edge (717 nm).
The LiDAR data were collected by two terrestrial laser scanners
operating at different laser wavelengths (Table 1). The Trimble GS200
scanner had a green laser with a 532 nm wavelength (Fig. 2a), while the
Leica scanner had a red laser with a 658 nm wavelength (Fig. 2b).
Collected data were in the form of a point cloud with each point pos­
sessing an X-, Y-, and Z-coordinate and an intensity value.
3.2. Sample preparation
The experimental sample set was comprised of three common
building materials: concrete, brick, and mortar with a range of compo­
sitions to cover some of the range of available material configurations.
Additionally, each class contained sub-classes of materials. For the
concrete, there were 54 samples, with 18 each of 3 different water
cement ratios (50%, 65%, and 80%). They produced according to ASTM
standard C 192-90a [39] and were wholly identical, except for the water
to cement ratio (Table 2). The dimensions of each concrete sample were
approximately 50 × 50 × 50 mm.
The brick samples were produced from two common types of clay:
red and yellow firing clay. The bricks were machine pressed and deliv­
ered unfired by the Vandersanden brick factory of Bilzen, Belgium
2
Fig. 1. RedEdge MicaSense Multispectral camera (support.micasense.com).
Z. Zahiri et al.
Table 1
Laser scanner specifications.
Journal of Building Engineering 44 (2021) 102603
Specifications
Trimble GS200™
Leica Scan Station P20
Laser colour
Laser wavelength
Point accuracy
Field of view
Green
532 nm
3 mm @ 100 m
360◦ × 60o
Red
658 nm
3 mm @ 50 m; 6 mm @ 100 m
360◦ × 60o
Table 2
Characterization of concrete mixes.
Water/Cement Ratio
(%)
Density (kg/
m3)
Compressive Strength
(MPa)a
Slump
(mm)
50%
65%
80%
2424.67
2365.83
2359.67
35.72
26.78
15.25
82
117
197
a
Averaged from three cube 10 cm × 10 cm × 10 cm specimens according to
BS 1881-116:2002.
Table 3
Component materials of the tested brick.
Composition*
Size (mm)
Factory
Code
Compressive
Strength**
(MPa)
Red
firing
brick
Yellow
firing
brick
Red firing local
loam, white firing
German clay
Yellow firing local
loam, red firing
local loam, and
chalk
190 × 90 × 50
D24-S315077
11.40
210 × 100 × 65
D08-S315087
15.00
*As reported by the manufacturer.
**Average from three samples when fired at 1060 ◦ C.
Fig. 2. Laser scanners used for collecting LiDAR data. a) Trimble laser scanner.
b) Leica laser scanner.
(Table 3). Prior to firing, the bricks were cut by the authors into small
cubes (roughly 40 × 40 × 40 mm) providing a total of 117 samples
across the 2 brick types (63 red brick samples and 54 yellow brick
samples). The samples were divided into 3 groups, with each fired at a
different temperature: 700 ◦ C, 950 ◦ C, and 1060 ◦ C. For each firing level,
there were 21 red brick samples and 18 yellow brick samples.
A total of 42 mortar samples were used. Half were lime mortar and
the other half Type S mortar [40], as described in Table 4. The lime
mortar was made using a lime putty from Cornish Lime Co. LTD Bodmin,
Cornwall, UK. As the material was already in a paste form, only a small
amount of additional water was added to achieve good workability. For
the mortar Type S, Portland cement was mixed with natural hydraulic
lime (obtained from The Lime Store, Dublin. Ireland). The hydraulic
lime, cement, and sand were mixed in a small counter-top mixer, and
then the water was added gradually, until the mix obtain a good
workability. The mortar was cast in cubes 40 × 40 × 40 mm and cured at
room temperature.
The calibration and validation samples for the classification models
were assembled as two, separate, dry-laid block walls (Fig. 3). Rows
1–3 at the bottom of each wall were concrete samples, with each row a
different water to cement (w/c) mix stacked from strongest to weakest
from the bottom up. The samples in rows 4–9 (from the bottom) were
brick samples comprised of 12 yellow and 14 red bricks in each row.
Within each colour group (rows 4–6 and 7–9), the bricks were placed in
clusters according to firing temperature from the least fired group (rows
4 and 7) to the most fired group (rows 6 and 9) [Fig. 3a]. The top two
rows of each wall were mortar samples, with the lime samples (row 10)
beneath mortar Type S (row 11) [Fig. 3a]. The validation wall was
composed in the same order with the remaining samples (1/3rd of the
total samples) (Fig. 3b). The two walls were scanned with the multi­
spectral camera and the two terrestrial laser scanners described in
Table 1. The light illuminance at all corners of the two walls was
measured with a ISO-TECH 1335 light meter registering 30 klux, thereby
confirming a uniform lighting (sunlight) across the samples.
Table 4
Mortar composition.
Mortar Type
Sand
Water
Cement
Lime
Type S
Lime
3
2.5
0.8
0.15
1
0
0.5
1
incorporating functions from the Image Processing and Statistics tool­
boxes and additional functions written in-house.
4.1. Multispectral integration
As previously mentioned, the MicaSense multispectral camera has 5
sensors each of which collects reflected light intensity at specific band,
resulting in 5 different images. Due to small offsets of the sensors on the
camera’s face, the position from which the data were taken differs
slightly with respect to the angle of incidence. Hence, the images were
registered and aligned using “imregconfig” function, and later they were
combined using the “cat” function in Matlab to create a cube of a fixed
length and width for all images and at a fixed depth of five bands.
4.2. LiDAR and multispectral integration
4. Data processing
All data analysis was conducted using MATLAB (release R2014b)
Brick
Type
3
The LiDAR point clouds from the two different terrestrial laser
scanners were converted into a pair of two-dimensional planes with only
x- and y-coordinates along with intensity data for each point (Fig. 4a).
Then the planes were divided into grids such that the number of grids in
the x- and y-directions were equal to the number of pixels in the length
and width of the multispectral image (Fig. 4b). Finally, the mean in­
tensity value of all the points confined in each grid was calculated and
displayed as a pixel with spatial positions corresponding to the pixels in
the multispectral image (Fig. 4c).
As building components may appear similar but be of different ma­
terials, the experiment was designed to exclude the geometry as that
cannot be relied upon. For this, samples of identical shapes and surface
properties were produced so that the only criteria by which they can be
identified would be their electromagnetic behavior and not some
geometrical characteristic which might appear in the field in the form of
Z. Zahiri et al.
Journal of Building Engineering 44 (2021) 102603
Fig. 3. Specimen input (213 samples in total).
Fig. 4. Converting Trimble 3D point cloud of Trimble scanner to 2D intensity plane.
variable z values. Additionally, the intensity data were not converted to
reflectance data, because the scanning area was relatively small (less
than 1 m wide) and little variance was seen. More information about the
data integration is provided in Appendix A.
The intensity values of the LiDAR data (29–255 from the Trimble and
− 1140–1514 from the Leica) and multispectral data (7168–63,175)
were all normalized individually between 0 and 1 (by subtracting the
minimum value from all values and dividing them by the difference
between the minimum and maximum values). Then the LiDAR image
planes were combined with the five spectral image planes of the mul­
tispectral images using the “cat” function in Matlab resulting in a cube of
a fixed length and width and at a fixed depth of seven sensor data sets
(instead of the previous five) [Fig. 5]. With both multispectral and
LiDAR technologies, the sensors measure the intensity of light reflected
from the target object. Hence, to prevent confusion, the readings in all
Fig. 5. Combining multispectral and LiDAR data.
bands (multispectral and LiDAR) are referred to as “intensity”
throughout this document.
Each sample contained at least 200 pixels. For each sample in the
wall, a region of 5 × 5 pixels (a total of 25 pixels) was selected from the
sample’s centre. The mean, multi-sensor spectrum of these pixels was
considered as the representative spectrum of that sample. This resulted
in 213 spectra for the 213 samples. For further investigation of the
created classification models, the models were applied to all pixels in the
validation image. Standard Normal Variate (SNV) pre-treatment (as
described in Ref. [41] was applied to the multi-sensor data to attempt to
correct for any surface induced variations in the measured signal.
4.3. PLSDA classification models
In this study, Partial Least Squares Discriminative Analysis (PLSDA)
[42] was used to classify the brick, concrete, and mortar samples. PLSDA
is a supervised classification technique based on PLS regression (PLSR)
to find the relation between two matrices (X and Y), in which X is the
information measured for each sample (i.e. the spectra of the samples)
and Y is a column vector defining the class membership for each sample
in X. The Y variable is 1 for “in class” samples and 0 for “out of class”
samples. For instance, if a PLSDA model is built to detect the brick class,
the Y variable is 1 for brick samples and 0 for non-brick (i.e. concrete
and mortar) classes. With this, five classification models were built
(Fig. 6 and Table 5). The first model was to classify the three main
building materials from each other. The other four models were gener­
ated to distinguish classes within each material group. Models were built
on both raw and SNV processed data.
4
Z. Zahiri et al.
Journal of Building Engineering 44 (2021) 102603
Fig. 6. Graphical description of different classification models.
is provided in Table 7, where a plus indicates an improvement, a minus
sign a worsening in the results, and an equal sign no change.
Table 5
Sample quantities in the calibration and validation sets of each model.
Model
Calibration Samples
Validation Samples
1
142 (36 concrete, 78 brick, and 28
mortar)
78 (36 yellow and 42 red)
78 (26 for each firing level)
28 (14 for each mortar)
36 (6 for each w/c ratio)
71 (18 concrete, 39 brick, and 14
mortar)
39 (18 yellow and 21 red)
39 (13 for each firing level)
14 (7 for each mortar)
18 (6 for each w/c ratio)
2
3
4
5
5.2. Applying classification models to pixels in validation wall
5. Results
5.1. Assessment of classification model performance
Table 6 summarizes the correct classification rates (CCRs) achieved
for the validation samples across all models. For each classification
model, four different variations of the data were considered: a) multi­
spectral data without pre-treatment, b) multispectral with pretreatment, c) multispectral and LiDAR without pre-treatment, and d)
multispectral and LiDAR with pre-treatment. The main observation from
Table 6 is that the use of SNV pre-treatment on the data (both multi­
spectral data and the combined multispectral and LiDAR data)
decreased the classification accuracy in Model 2 (brick type) and Model
3 (brick firing level) by at least 10%. While, using the pre-treated data
improved the classification of Model 5 (concrete with different w/c) and
had little impact on the classification results of Model 1 and Model 4. In
general, the SNV pre-treatment worsened results among the 5 models
from 81.08% to 76.60% for multispectral data and from 80.85% to
78.18% for the combined LiDAR and multispectral data. This decrease in
CCR after applying SNV might be due to the similarities of spectral shape
among classes; as described in Appendix B.
Table 6 also shows that when LiDAR data were added and no pretreatment occurred, the classification models improved for Model 2
(brick type) and Model 4 (mortar type) at 5% and 7% respectively.
However, including LiDAR data decreased the accuracy of Model 3
(brick firing levels) and Model 5 (concrete) by 5% and 10%, respec­
tively. The average CCR of the 5 models with the addition of the LiDAR
data improved only marginally from 82.76% to 83.07% (Table 6).
A summary of the impact of the data sources and SNV pre-treatment
The classification models were later applied to the entirety of the
pixels in the validation set to visualise the performance of the models. In
the making of the previous models, the mean spectrum of the 25 pixels in
the centre of each sample was used for testing the models. However, in
this instance, the models were applied to all the pixels including the 25
pixels in the centre of the samples. Using the larger data set produced the
following results. For the first 3 classification models, better results were
achieved when the LiDAR data were added to the multispectral images
without applying SNV (Model 1c, 2c, and 3c in Fig. 7). Among these 3
models, the best result was obtained for Model 1 (85.63%), with nearly
all bricks (99.31%), 85.92% of concrete, and 71.65% of the mortar
properly classified (n.b. all mortar misclassifications appeared as con­
crete). However, the mortar samples were better classified with SNV pretreatment (78.45%) [Model 1d in Fig. 7].
The next best result was obtained for Model 2 when the LiDAR data
were added (Model 2c in Fig. 7). Similarly, SNV pre-treatment reduced
the overall CCR of Model 2 (Model 2b and Model 2d compared to Model
2a and Model 2c in Fig. 7). This improvement of adding the LiDAR data
was especially notable in the red brick class resulting in an improvement
in CCR from 73.58% to 79.34% and with better segmentation for the
top-most row in red bricks (Model 2c in Fig. 7). In Model 3 for the brick
firing level, the CCR varied significantly between the red and yellow
bricks. However, similar to Models 1 and 2, the best overall CCR for this
Table 7
Summary of the impact of the data sources and pre-treatment approaches on the
CCRs.
Classification
models
a) Base
Condition
(multispectral)
b) No LiDAR
with SNV
c) LiDAR
without SNV
d) LiDAR
with SNV
Model 1
Model 2
Model 3
Model 4
Model 5
95.77%
92.31%
87.18%
85.71%
44.44%
þ
þ
þ
þ
þ
-
þ
¼
Table 6
Correct classification rate (CCR %) for both multispectral and combined validation sets before and after SNV pre-treatment with best results shown per model in bold.
Data
Model 1 (3 materials)
Model 2 (Brick type)
M*
Model 3 (Firing level)
No SNV^
95.77
92.31
87.18
With SNV
98.59
76.92
71.79
Average of best results from 5 classification models for multispectral data
M & L**
No SNV
98.59
97.43
82.05
With SNV
97.18
84.61
71.82
Average of best results from 5 classification models for combined multispectral and LiDAR data
5
*M: Multispectral **L: LiDAR ^SNV: Standard normal variate pre-treatment.
Model 4 (Mortar type)
Model 5 (Concrete w/c)
Average
85.71
85.71
44.44
50
92.85
92.85
33.33
44.44
81.08
76.60
82.76
80.85
78.18
83.07
Z. Zahiri et al.
Journal of Building Engineering 44 (2021) 102603
Fig. 7. Classification images of 5 models for different data sets with and without SNV with best results of each dataset (considering SNV effect) in bold text and the
average of the best overall results presented for each technology (M and M&L)
*M: Multispectral **L: LiDAR ^SNV: Standard Normal Variate pre-treatment.
model (52.24%) was also obtained after adding the LiDAR data and
without applying SNV pre-treatment. Clear segmentation can be
observed for most samples, except for yellow brick samples fired at
700 ◦ C (Model 3c in Fig. 7).
Model 4 for classifying mortar classes resulted in a slight reduction in
the overall CCR after adding the LiDAR data (66.76% vs. 65.53%).
Although the segmentation of lime mortar improved by 8% after adding
the LiDAR data, the one for Type S mortar worsened (11% lower CCR, as
shown in Model 4a vs. Model 4c in Fig. 7). With both data types, the SNV
pre-treatment slightly reduced the overall CCR (Model 4b and Model 4d
6
in Fig. 7). The combined LiDAR and multispectral data set had a mixed
impact on Model 5 for differentiating concrete classes. Similar to Models
1–3, the best result (44.27%) in Model 5 was obtained after adding
LiDAR data but with SNV pre-treatment (Model 5d in Fig. 7). In this
model (Model 5d), 56.82% of the concrete class 50% was classified
correctly. Notably, the results for concrete classes 65% and 80% were
poorer (41.58% and 34.41% respectively). Averaging the best results
obtained with multispectral data (irrespective of processing approaches)
was 62.95%, an increase of more than 2%–65.13%. In general, the
concrete classification model was the worst among the 5 models. Table 8
Z. Zahiri et al.
Table 8
Data type resulting in the best CCR for each of the 5-classification models.
Best
Results
Model 1 (3
materials)
Model 2
(Brick
type)
Model 3
(Firing
level)
Model 4
(Mortar
type)
Model 5
(Concrete
w/c)
Mean
spectra
of
samples
Pixel data
M* with
SNV^
M&L**
M&L
M
M&L
M with SNV
M&L
M&L
M&L
M
M&L with
SNV
Journal of Building Engineering 44 (2021) 102603
*M: Multispectral **L: LiDAR ^SNV: Standard Normal Variate pre-treatment.
Fig. 8. Comparing the PLSDA classification performance of different spec­
tral data.
shows the approach that generated the best outputs when applied to the
mean spectra of samples’ central regions and to the pixel data. In both
methods, most of the models worked better when multispectral data
were combined with the LiDAR data.
better (Table 9).
The bar chart in Fig. 8 compares the classification results from
spectrometry data with more than 200 bands in short-wave Infrared
[33–35], RedEdge multispectral data in Vis-NIR (5 bands), and com­
bined multispectral and LiDAR intensity data (7 bands). These results
demonstrate that while spectrometry data in SWIR with more than 200
bands obtained the highest performance, the multispectral data (either
exclusively or combined with LiDAR intensity) perform similarly, except
for concrete classification (Model 5). The poor results of the approach in
concrete classification were also due to the fact that multi-sensor data
were mostly within visible range and NIR range, which are insensitive to
water. In future, utilizing laser scanners with an Infrared laser (e.g a
ILRIS laser scanner) or including more SWIR wavelengths in the
multi-spectral sensor might improve the classification results, especially
when desiring to classify concrete based on water content.
Although the overall results of most classification models improved
slightly by adding LiDAR data (Table 8 and Fig. 7), the capability of the
MicaSense multispectral data exclusively, was confirmed to detect dif­
ferences in material samples (Fig. 8). Hence, the multispectral imaging
with MicaSense RedEdge camera presented a compelling approach to be
the basis for material classification, especially given its limited spectral
resolution, significantly easier set up compared to a laser scanner, and its
rapid data capturing ability.
While these observations provide useful insights into possible
equipment based means to classify building materials in a nondestructive, non-contact manner, to be able to more rigorously gener­
alize the observations will require extensive additional investigations
that combination of close range non-destructive testing and destructive
chemical analysis (e.g. XRD, TGA, etc.) beyond the scope of this study.
6. Discussion
The CCRs in this study varied among the different classification
models by (1) using exclusively multispectral data, (2) combining them
with LiDAR intensity data, and (3) applying SNV pre-treatment. How­
ever, overall, the combination of multispectral and LiDAR data resulted
in the best CCR in most classification models (Table 8) and marginally
improved the average CCR of all models (82.76%–83.07%). Previous
work for land cover classification has also shown that integration of
LiDAR intensity data and multispectral marginally increased the clas­
sification accuracy from 82.5% to 83.5% [43]. In that research, inclu­
sion of LiDAR intensity improved the classification for water (from 90%
to 100%) and pavement (from 68% to 88%) but significantly decreased
classification accuracy of bare ground (from 80% to 52%). Similarly, in
the study herein, the introduction of LiDAR intensity data improved
classification of some materials but not uniformly. However, the often
marginal and inconsistent results achieved with the introduction of the
LiDAR intensity data indicate that further research is needed to deter­
mine whether the relative benefits outweigh the extra difficulties and
costs associated with collecting data from multiple remote sensing de­
vices and the subsequent storage and processing, as well as the most
beneficial wavelength(s) for the LiDAR.
To determine whether the results could be improved through a
different modelling approach, a Support vector machine (SVM) classi­
fier, which is commonly used for image classification, was built from the
same training data and was applied to the test data for further analysis.
In SVM, a hyperplane is used to linearly separate the higher dimensional
data. Non-linear data in the original dimension is mapped to linearly
separable higher dimensional space [44].
SVM could achieve 100% accuracy for all types and combinations of
data in Model 1 (Table 6 vs. Table 9). In Model 2, for classifying brick
types, SVM only slightly improved the results from Multispectral data
without pre-treatment but worsened or did not change the results for
other types of data. The results of Model 3, Model 4 and Model 5 also
worsened by using SVM classifier for almost all data. In general, the
results from SVM classification were worse for most of the models and
proved that the PLSDA classifier, which was considered initially, works
7. Conclusions
Multispectral images were considered with and without intensity
data from RedEdge multispectral camera and two LiDAR units operating
at different emitting wavelengths in an attempt to classify different
building materials (concrete, brick, and mortar). A PLS-DA approach
applied to the data was capable to classify main material classes (con­
crete, brick, and mortar) and then differentiate distinctions within each
certain material class (different brick, mortar, and concrete types).
There were promising results in the models related to mortar type, brick
Table 9
Correct classification rate (CCR %) results achieved by SVM classifier.
Data
M*
M & L**
No SNV^
With SNV
No SNV
With SNV
Model 1 (3 materials)
Model 2 (Brick type)
Model 3 (Firing level)
Model 4 (Mortar type)
Model 5 (Concrete w/c)
Average
100
100
100
100
94.87
82.05
92.31
84.62
71.79
69.23
82.05
64.10
57.14
57.14
57.14
57.14
33.33
33.33
33.33
33.33
71.43
68.35
72.97
67.84
*M: Multispectral **L: LiDAR ^SNV: Standard Normal Variate pre-treatment.
7
Z. Zahiri et al.
clay type, and brick firing levels. Poorer results were obtained dis­
tinguishing concretes made with different water to cement ratios. The
results also showed that in most cases, multispectral data (either
exclusively or combined with LiDAR data) obtained exceed 80% for
correct classification rates. This proves the potential for using RedEdge
multispectral camera for detecting changes in building materials and
that such technologies may be able to estimate mechanical properties of
material via a non-destructive, non-contact technique. Such an ability
would represent a major breakthrough in building conservation with
respect to identifying and distinguishing in situ materials.
To determine the full value of this type of work, future experimental
efforts should be undertaken for brick with additional clay types and
compositions, mortars of different aggregates and lime contents, and
concretes with other cements and aggregate types, as well as with
plasticizers and retarders and those with long-term exposure to
pollutant. This technique should also be assessed under varying levels of
sunlight noting, however, that the multispectral camera used in this
study requires direct sunlight for capturing proper images in the NIR and
RedEdge bands.
Mr. John Ryan from UCDJournal
technician
team, as well as Mr. Bert Neyens
of Building Engineering 44 (2021) 102603
(Vandersanden Group) for their support and assistance in conducting
the experimental parts of the study. This work was supported by New
York University’s Center for Urban Science and Progress; and The Eu­
ropean Research Council (ERC) [grant number ERC-2013-StG-335508—
BioWater].
Author statement
Zohreh Zahiri undertook the conceptualization, methodology, vali­
dation, formal analysis, investigation, writing, visualization.
Debra Laefer provided the conceptualization, resources, writing,
visualization, supervision, project administration, and funding
acquisition.
Aoife Gowan contributed to the methodology, software, reviewing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The authors wish to thank Mr. Donal Lennon, Mr. Derek Holmes, and
Appendix A. Concatenation
In this study, a multispectral image of the wall with fixed dimensions (x*y) was used where x is the number of pixels in the x direction and y is the number
of pixels in the y direction. The data collected by laser scanners are not pixel-based but are, instead, parts of point clouds with thousands of points indicating
the x, y, z coordinates, as well as the intensity values for each point. To be able to combine these two data sets (multispectral image and LiDAR point clouds),
the three-dimensional point cloud data had to be projected into two-dimensional planes, as mentioned in the main text. This was done by making a mesh with
the exact number of x and y pixels as that which appears in the multispectral image and by computing the average intensity of all the points within every cell of
this mesh. In this case, the two resulting planes are of the same dimensions of the multispectral image, which enables a co-registration.
The ’cat’ function is the shortened name for concatenate and is a function in Matlab, which was used to concatenate two matrices along a certain
dimension. For example, C = cat(dim,A,B) concatenates B to the end of A along dimension dim. The aim was to concatenate the multispectral matrix
and the LiDAR planes not along the x or y dimensions, but along the 3rd dimension, which is the intensity values [MnL = cat(3,Multispectral, LiDAR)].
For the multispectral image, there are 5 different intensity matrices (5 spectral bands), and for the LiDAR planes, there are 2 different intensity
matrices (2 spectral bands from 2 different laser scanners). Hence, after concatenating, the MnL matrix has fixed x and y dimensions, the 3rd
dimension is now 7. While the Cat function does not explicitly introduce error, there is a marginal concatenating error in combining the two datasets,
because they are completely different data types (one is a 2D image, while the other 3D point cloud). By checking the edges of the wall, the error was
established to be less than 5 mm in the length in both the calibration and validation walls (60 cm and 30 cm, respectively).
Appendix B. Additional Results
B1.1. Model 1 classification results (classifying main materials)
After scanning with both techniques (i.e. multispectral and LiDAR), the mean spectra for the brick, concrete, and mortar samples were calculated
Fig. B1. Mean spectra of materials in Model 1.
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Journal of Building Engineering 44 (2021) 102603
Fig. B2. Validation results of Model 1 for classifying main materials B: Brick C: Concrete M: Mortar.
and plotted (Fig. B1). In general, the mean spectra of the three main material classes showed distinctive patterns in the multispectral bands, with the
mortar and concrete spectra showing higher intensities (especially in the blue and green bands) compared to the brick spectra (Fig. B1a). Less effective
for the three materials were the Near IR and RedEdge bands (around 0.2) where less distinction was apparent, except with the mortar in the RedEdge
band.
After adding the LiDAR data, the brick and mortar exhibited similar intensity in the Leica (wavelength 658 nm) and Trimble (wavelength 532 nm)
Fig. B3. Mean spectra of yellow and red bricks.
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Journal of Building Engineering 44 (2021) 102603
Fig. B4. Validation results of Model 2 for classifying yellow and red bricks (Y: Yellow brick R: Red brick).
Fig. B5. Mean spectra of bricks at different firing level.
bands (around 0.65 and 0.8 respectively), but the concrete exhibited more distinguishable intensities [Fig. B1a]. This further distinction in the
concrete spectra with the LiDAR bands justifies the improvement in classification of concrete and mortar and a slight increase in the overall CCR by
adding LiDAR data (from 95.77% to 98.59%). When SNV pre-treatment was applied to the multispectral data, the brick spectra was distinctive among
the materials (Fig. B1b). Specifically, better separation between spectra was observed in the Leica (L) and Trimble (T) data in Fig. B1b.
With Model 1, when only the multispectral data were used, 3 of the 71 samples were misclassified without the SNV (Fig. B2a) and 1 with the SNV
(Fig. B2b). With the LiDAR data, there was 1 misclassification both with and without the SNV pre-treatment (Fig. B2c and Fig. B2d). Interestingly, in
all cases, the misclassification was only between the mortar and concrete, but in each instance different samples were misclassified.
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B1.2. Model 2 classification results (classifying yellow and red bricks)
Journal of Building Engineering 44 (2021) 102603
The mean spectra of each of the two brick classes (Model 2) are plotted in Fig. B3. Despite having similar patterns in the multispectral bands, the
red brick had lowere intensities than the yellow bricks. However, the intensities from the yellow and red bricks became closer to each other in the Near
IR and RedEdge bands (Fig. B3a). Similarly, when the LiDAR data were added, distinctive behaviour was observed in spectra with higher intensities for
the yellow brick (Fig. B3a). When SNV was applied to the multispectral data, the offset between the spectra disappeared, and they became closer to
each other, especially in the blue and red bands (Fig. B3b). Similarly, the SNV pre-treatment made the spectra of two brick types very close to each
other in the LiDAR bands (Fig. B3b).
When Model 2 was built on the multispectral data (Model 2a), all yellow bricks were classified correctly, but 3 of the 21 red bricks were mis­
classified as yellow bricks without the SNV (Fig. B4a). Misclassification increased four-fold to 12 when SNV pre-treatment was applied (Fig. B4b). The
addition of the LiDAR data reduced the misclassification to a single instance (Fig. B4c). SNV pre-treatment also had a worsening effect and resulted in 6
misclassified samples (Fig. B4d). So, while the LiDAR data improved the classification, the SNV did not. Notably, most misclassified bricks were the red
ones fired at the highest temperature. The RGB image displayed a yellowing of these samples, which may in part explain the misclassification.
B1.3. Model 3 classification results (classifying 3 levels of brick firing)
The mean spectra of bricks fired at different temperature are plotted in Fig. B5. Despite similar spectra, the mean spectra were still distinguishable
[especially in the Green, Red, and RedEdge bands (Fig. B5a)]. When the LiDAR data were added, bricks fired at 700 ◦ C and 950 ◦ C showed almost the
same intensity in the Leica and Trimble bands (Fig. B5a). This might explain the reduction in CCR after adding LiDAR data. In general, the spectra
showing the brick at the three firing levels were very similar in both multispectral and combined data sets. After applying SNV to the multispectral and
combined data sets, the spectra became even closer to each other and less distinguishable (Fig. B5a vs Fig. B5b), thus accounting for the poorer results
for Model 3 after applying SNV pre-treatment.
Initially, for the 700 ◦ C firing class data, 9 of the 13 samples were correctly classified with just the multispectral data (Fig. B6a). This decreased to
only 7 when the LiDAR data were added (Fig. B6c). No misclassification was observed with the bricks fired at 950 ◦ C, and only 1 sample was mis­
classified for the bricks fired at 1060 ◦ C with both multispectral and combined datasets (Fig. B6a vs. Fig. B6c). When SNV pre-treatment was applied,
the number of misclassified samples increased in the multispectral data (Model 3b) in both the 700 ◦ C and 950 ◦ C classes but remained unchanged in
the 1060 ◦ C class, with only 1 misclassified sample (Fig. B6b). The negative impact of SNV pre-treatment was even worse in Model 3d with the
Fig. B6. Validation results of Model 3 for classifying brick firing level.
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Journal of Building Engineering 44 (2021) 102603
Fig. B7. Mean spectra of mortar classes.
combined dataset (Fig. B6d). While bricks at 700 ◦ C got relatively better segmentation, the misclassifications samples increased for bricks fired at
higher temperatures (950 ◦ C and 1060 ◦ C).
Fig. B8. Validation results of Model 4 for classification of mortar.
B1.4. Model 4 classification results (classifying 2 mortar classes)
The Type S and lime mortars were similar in most multispectral bands, except in the Blue and Green bands, with the lime mortar having higher
intensity (Fig. B7a). The addition of LiDAR data changed this, with higher intensity exhibited in the Leica and Trimble bands (Fig. B7a). This may
explain the classification improvement (from 85.71% to 92.85%) with the introduction of the LiDAR data. Significant change was not observed in the
spectra of the mortar after applying SNV (Fig. B7a vs. Fig. B7b). With the multispectral data, two lime mortar samples were misclassified as mortar
Type S, both before and after pre-treatment (Fig. B8a vs. Fig. B8b). Incorporation of the LiDAR data corrected one of the misclassified samples
(Fig. B8c). In contrast, application of SNV pre-treatment had no impact on the classification results (Fig. B8d).
B1.5. Classification results of model 5 (classifying 3 concrete classes)
The concrete spectra behaved very similarly in the multispectral bands, except in the Blue and Green bands (Fig. B9a). The intensity of the concrete
class 50% was highest in the Green band, while in the Blue band this was almost the same as that of concrete class 80% and slightly more than concrete
class 65% (Fig. B9a). When the LiDAR data were added, the same trend happened
in the Trimble band (532 nm), with the higher intensity values for
12
concrete classes 50% and 80% (Fig. B9a). The application of SNV pre-treatment had no notable effect on the spectra in multispectral bands but slightly
Z. Zahiri et al.
Journal of Building Engineering 44 (2021) 102603
Fig. B9. Mean spectra of concrete.
Fig. B10. Validation results of Model 5 for concrete classification.
13
Z. Zahiri et al.
increased the intensity of concrete class 65% in the Leica band of 658 nm (Fig. B9b).
Journal of Building Engineering 44 (2021) 102603
The initial PLS-DA model on multispectral data (before applying SNV) successfully classified all concrete class 65% samples (Fig. B10a) but only
two samples from concrete class 50% and none from concrete class 80%. SNV pre-treatment doubled the correct classification rate of concrete class
50% and enabled 5 of the 6 samples from concrete class 80% to be predicted correctly but then failed with all concrete 65% samples (Fig. B10b). When
LiDAR data were added, all concrete samples were predicted in concrete class 65% (Fig. B10c). After applying SNV to the combined dataset, 5 samples
from concrete class 50%, only 3 samples in concrete class 65% were classified correctly and none in concrete class 80% (Fig. B10d). In summary, the
results of the models were very mixed across the three concrete classes, with none working well across all samples. The inconsistent results from all
versions of Model 5 might be related to the very similar spectra of these three concrete classes (Fig. B9) in the limited number of bands that were
available.
References
[21] J.M. Grunicke, G. Sacher, B. Strackenbrock, Image processing for mapping
damages to buildings, in: CIPA International Symposium, Cracow, Poland, 1990,
pp. 257–263.
[22] J. Herráez, P. Navarro, J.L. Lerma, Integration of normal colour and colour Infrared
emulsions for the identification of pathologies in architectural heritage using a
digital photogrammetric system, Int. Arch. Photogram. Rem. Sens. (1997)
240–245. XXXII(5C1B).
[23] J.L. Lerma, L.Á. Ruiz, F. Buchón, Application of spectral and textural classifications
to recognize materials and damages on historic building facades, Int. Arch.
Photogram. Rem. Sens. (2000) 480–484. XXXIII(B5).
[24] J.L. Lerma, Multiband versus multispectral supervised classification of
architectural images, Photogramm. Rec. 17 (97) (2001) 89–101.
[25] W.Y. Yan, A. Shaker, N. El-Ashmawy, Urban land cover classification using
airborne LiDAR data: a review, Remote Sens. Environ. 158 (2015) 295–310.
[26] B. Höfle, M. Hollaus, J. Hagenauer, Urban vegetation detection using
radiometrically calibrated small-footprint full-waveform airborne LiDAR data,
ISPRS J. Photogrammetry Remote Sens. 67 (2012) 134–147.
[27] G. Tolt, M. Shimoni, J. Ahlberg, A shadow detection method for remote sensing
images using VHR hyperspectral and liDAR data, in: Proceedings of International
Geoscience and Remote Sensing Symposium (IGARSS), Canada, Vancouver, 2011,
pp. 4423–4426, 25-29 July.
[28] M. Idrees, H.Z.M. Shafri, V. Saeidi, Imaging spectroscopy and light detection and
ranging data fusion for urban features extraction, Am. J. Appl. Sci. 10 (12) (2013)
1575–1585.
[29] G. Sithole, Detection of bricks in a masonry wall, Int. Arch. Photogram. Rem. Sens.
Spatial Inf. Sci. (2008) 567–571. XXXVII(B5).
[30] M. Hemmleb, F. Weritz, A. Schiemenz, A. Grote, C. Maierhofer, Multispectral data
acquisition and processing techniques for damage detection on building surfaces,
Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 36 (B5) (2006) 6.
[31] S. Morsy, A. Shaker, A. El-Rabbany, Multispectral LiDAR data for land cover
classification of urban areas, Sensors 17 (5) (2017) 958.
[32] L. Yuan, J. Guo, Q. Wang, Automatic classification of common building materials
from 3D terrestrial laser scan data, Autom. ConStruct. 110 (2020) 103017.
[33] Z. Zahiri, D.F. Laefer, A. Gowen, The feasibility of short-wave infrared
spectrometry in assessing water-to-cement ratio and density of hardened concrete,
Construct. Build. Mater. 185 (2018) 661–669.
[34] D.F. Laefer, Z. Zohreh, A. Gowen, Using short-wave infrared range spectrometry
data to determine brick characteristics, Int. J. Architect. Herit. (2018), https://doi.
org/10.1080/15583058.2018.1503362.
[35] Z. Zahiri, D.F. Laefer, A. Gowen, Classification of Hardened Cement and Lime
Mortar Using Short-Wave Infrared Spectrometry Data. Structural Analysis of
Historical Constructions, Springer, Cham, 2019, pp. 437–446.
[36] W. Liao, A. Pizurica, P. Scheunders, W. Philips, Y. Pi, Semi supervised local
discriminant analysis for feature extraction in hyperspectral images, IEEE Trans.
Geosci. Rem. Sens. 51 (1) (2013) 184–198.
[37] D.L. Donoho, High-dimensional data analysis: the curses and blessing of
dimensionality, in: AMS Math. Challenges 21st Century, 2000.
[38] S. Chen, D. Laefer, E. Mangina, State of technology review of civilian UAVs,
Engineering Patent Journal, Bentham 10 (3) (2016) 160–174.
[39] Astm C192-90a, Standard Practice for Making and Curing Concrete Test Specimens
in the Laboratory, American Society Testing & Materials, USA, 1990.
[40] Astm C270-14a, Standard Specification for Mortar for Unit Masonry, ASTM
International, West Conshohocken, PA, 2014. www.astm.org.
[41] C. Esquerre, A.A. Gowen, J. Burger, G. Downey, C.P. O’Donnell, Suppressing
sample morphology effects in near infrared spectral imaging using chemometric
data pre-treatments, Chemometr. Intell. Lab. Syst. 117 (2012) 129–137.
[42] P. Geladi, B.R. Kowalski, Partial least-squares regression: a tutorial, Anal. Chim.
Acta 185 (1986) 1–17.
[43] K.A. Hartfield, K.I. Landau, W.J.D. Van Leeuwen, Fusion of high resolution aerial
Multispectral and LiDAR data: land cover in the context of urban mosquito habitat,
Rem. Sens. 3 (2011) 2364–2383.
[44] V. Sowmya, K.P. Soman, M. Hassaballah, Hyperspectral image: fundamentals and
advances, in: Recent Advances in Computer Vision, Springer, Cham, 2019,
pp. 401–424.
[1] L.J. Sánchez-Aparicio, B. Riveiro, D. Gonzalez-Aguilera, L.F. Ramos, The
combination of geomatic approaches and operational modal analysis to improve
calibration of finite element models: a case of study in Saint Torcato Church
(Guimarães, Portugal), Construct. Build. Mater. 70 (2014) 118–129.
[2] H. Aljumaily, D.F. Laefer, D. Cuadra, Big-Data approach for three-dimensional
building extraction from aerial laser scanning, J. Comput. Civ. Eng. 30 (3) (2015),
04015049.
[3] S. Zolanvari, D.F. Laefer, Slicing method for building facade extraction from LiDAR
point clouds, ISPRS J. Photogrammetry Remote Sens. 119 (2016) 334–346.
[4] M. Cabaleiro, J. Hermida, B. Riveiro, J.C. Caamaño, Automated processing of dense
points clouds to automatically determine deformations in highly irregular timber
structures, Construct. Build. Mater. 146 (2017) 393–402.
[5] S. Kotthaus, T.E. Smith, M.J. Wooster, C.S.B. Grimmond, Derivation of an urban
materials spectral library through emittance and reflectance spectroscopy, ISPRS J.
Photogrammetry Remote Sens. 94 (2014) 194–212.
[6] R. Ilehag, M. Weinmann, A. Schenk, S. Keller, B. Jutzi, S. Hinz, Revisiting existing
classification approaches for building materials based on hyperspectral data, Int.
Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 42 (2017).
[7] D. Dizhur, M. Giaretton, I. Giongo, K.Q. Walsh, J.M. Ingham, Material property
testing for the refurbishment of a historic URM building in Yangon, Myanmar, J.
Build. Eng 26 (2019) 100858.
[8] S. Chang, D. Castro-Lacouture, Y. Yamagata, Decision support for retrofitting
building envelopes using multi-objective optimization under uncertainties, J.Build.
Eng (2020) 101413.
[9] I. Torres, G. Matias, P. Faria, Natural hydraulic lime mortars-the effect of ceramic
residues on physical and mechanical behaviour, J.Build. Eng (2020) 101747.
[10] D. Breysse, X. Romao, M. Alwash, Z.M. Sbartaï, V.A. Luprano, Risk evaluation on
concrete strength assessment with NDT technique and conditional coring
approach, J.Build. Eng (2020) 101541.
[11] M.T. Marvila, A.R.G. Azevedo, J. Alexandre, E.B. Zanelato, N.G. Azeredo, N.
T. Simonassi, S.N. Monteiro, Correlation between the properties of structural clay
blocks obtained by destructive tests and ultrasonic pulse tests, J.Build. Eng 26
(2019) 100869.
[12] E. Chuta, J. Colin, J. Jeong, The impact of the water-to-cement ratio on the surface
morphology of cementitious materials, J.Build. Eng (2020) 101716.
[13] T.H. Kurz, J.S. Buckley, J.A. Howell, Close-range hyperspectral imaging for
geological field studies: workflow and methods, Int. J. Rem. Sens. 34 (5) (2013)
1798–1822.
[14] P. Hartzell, C. Glennie, K. Biber, S. Khan, Application of multispectral LiDAR to
automated virtual outcrop geology, ISPRS J. Photogrammetry Remote Sens. 88
(2014) 147–155.
[15] E.W. Bork, J.G. Su, Integrating LIDAR data and multispectral imagery for enhanced
classification of rangeland vegetation: a meta-analysis, Remote Sens. Environ. 111
(2007) 11–24.
[16] S. Yoga, J. Bégin, B. St-Onge, D. Gatziolis, Lidar and multispectral imagery
classifications of balsam fir tree status for accurate predictions of merchantable
volume, Forests 8 (7) (2017) 253.
[17] L. Palombi, D. Lognoli, V. Raimondi, G. Cecchi, J. Hällström, K. Barup, C. Conti,
R. Grönlund, A. Johansson, S. Svanberg, Hyperspectral fluorescence LiDAR
imaging at the Colosseum, Rome: elucidating past conservation interventions, Opt
Express 16 (10) (2008) 6794–6808.
[18] V. Raimondi, G. Cecchi, D. Lognoli, L. Palombi, R. Grönlund, A. Johansson,
S. Svanberg, K. Barup, J. Hällström, The fluorescence LiDAR technique for the
remote sensing of photoautotrophic biodeteriogens in the outdoor cultural
heritage: a decade of in situ experiments, Int. Biodeterior. Biodegrad. 63 (7) (2009)
823–835.
[19] R.P. Stumpf, K. Holderied, M. Sinclair, Determination of water depth with highresolution satellite imagery over variable bottom types, Limnol. Oceanogr. 48 (1)
(2003) 547–556.
[20] S.J. Winterbottom, D.J. Gilvear, Quantification of channel bed morphology in
gravel-bed rivers using airborne multispectral imagery and aerial photography,
River Res. Appl. 13 (6) (1997) 489–499.
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