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SAPC – APPLICATION FOR ADAPTING SCANNED ANALOGUE PHOTOGRAPHS TO USE THEM IN STRUCTURE FROM MOTION TECHNOLOGY

Authors:

Abstract

The documentary value of analogue scanned photographs is invaluable. A large and rich collection of archival photographs is often the only source of information about past of the selected area. This paper presents a method of adaptation of scanned, analogue photographs to suitable form allowing to use them in Structure from Motion technology. For this purpose, an automatic algorithm, implemented in the application called SAPC (Scanned Aerial Photographs Correction), which transforms scans to a form, which characteristic similar to the images captured by a digital camera, was invented. Images, which are created in the applied program as output data, are characterized by the same principal point position in each photo and the same resolution through cutting out the black photo frame. Additionally, SAPC generates a binary image file, which can mask areas of fiducial marks. In the experimental section, scanned, analogue photographs of Warsaw, which had been captured in 1986, were used in two variants: unprocessed and processed in SAPC application. An insightful analysis was conducted on the influence of transformation in SAPC on quality of spatial orientation of photographs. Block adjustment through aerial triangulation was calculated using two SfM software products: Agisoft PhotoScan and Pix4d and their results were compared with results obtained from professional photogrammetric software – Trimble Inpho. The author concluded that pre-processing in SAPC application had a positive impact on a quality of block orientation of scanned, analogue photographs, using SfM technology.
SAPC APPLICATION FOR ADAPTING SCANNED ANALOGUE PHOTOGRAPHS TO
USE THEM IN STRUCTURE FROM MOTION TECHNOLOGY
A. Salacha
a Faculty of Geodesy and Cartography, Institute of Photogrammetry, Remote Sensing and Spatial Information Systems, Warsaw
University of Technology, Warsaw, Poland adam.salach@pw.edu.pl
Commission II, WG II/4
KEY WORDS: aerial, archival, photographs, SfM, opencv
ABSTRACT:
The documentary value of analogue scanned photographs is invaluable. A large and rich collection of archival photographs is often
the only source of information about past of the selected area.
This paper presents a method of adaptation of scanned, analogue photographs to suitable form allowing to use them in Structure from
Motion technology. For this purpose, an automatic algorithm, implemented in the application called SAPC (Scanned Aerial
Photographs Correction), which transforms scans to a form, which characteristic similar to the images captured by a digital camera,
was invented. Images, which are created in the applied program as output data, are characterized by the same principal point position
in each photo and the same resolution through cutting out the black photo frame. Additionally, SAPC generates a binary image file,
which can mask areas of fiducial marks.
In the experimental section, scanned, analogue photographs of Warsaw, which had been captured in 1986, were used in two variants:
unprocessed and processed in SAPC application. An insightful analysis was conducted on the influence of transformation in SAPC on
quality of spatial orientation of photographs. Block adjustment through aerial triangulation was calculated using two SfM software
products: Agisoft PhotoScan and Pix4d and their results were compared with results obtained from professional photogrammetric
software Trimble Inpho. The author concluded that pre-processing in SAPC application had a positive impact on a quality of block
orientation of scanned, analogue photographs, using SfM technology.
1. INTRODUCTION
Within the past decade, digital cameras introduced to the market
have totally driven analogue photographs out of
photogrammetric production. However, the documentary value
of archival photographs is irrefutable. Vast collections of
analogue aerial photographs are often the only source of
information about a state of an object in the past. These datasets
are used more and more frequently by specialists in various
fields, e.g., archaeology, forestry or geography (Nocerino et al.,
2012; Kunz et al., 2012). Scanned analogue photographs are
usually processed into geo-referenced products: orthomosaics,
digital surface models (DSM) or point clouds. In environmental
and geographical applications, archival orthomosaics allow
change detection of the landscape over the years. Measurement
of terrain deformation or forest canopy growth is possible with
the use of historical DSM (Clery et al., 2014). The fact that the
above-mentioned products are more and more often found in GIS
databases is worth mentioning here.
Nonetheless, a standard approach to scanned analogue
photographs processing requires complex, professional
photogrammetric software (e.g., Trimble Inhpo, Hexagon
ImageStation) and expert knowledge. This fact excludes the
possibility of using the full potential of archival photographs by
users, who are not associated with photogrammetry in their daily
works. Photogrammetric software used for the aerotriangulation
process requires the knowledge of camera specification and
approximate values of exterior orientation parameters. In the case
of archival datasets, this information is hardly ever provided
together with scanned analogue photographs. An alternative
approach to archival photographs orientation which does not
require information from camera calibration report is the Direct
Linear Transformation (Ma, Buchwald, 2012). Unfortunately,
the disadvantage of this approach is a need of too many GCPs.
Clery, et al. (2014) suggest a method where scanned photographs
are registered one by one with a topographic database as
reference, however, according to the authors, expected quality
requirements have not been obtained yet. Software products,
based on Structure from Motion (SfM) technology and which
popularity is still growing (e.g., Agisoft PhotoScan, Pix4D), are
alternative for more expensive, professional photogrammetric
systems. Most SfM platforms are now fully automated and user-
friendly. SfM software producers are agreed that their software
products are not dedicated for scanned analogue photographs
processing, On the other hand, they claim that in some cases it is
possible to achieve satisfactory processing results. SfM holds
great promises for the quick 3D reconstruction from archival
data, but its accuracy is very much limited by the quality of the
scanned photographs (Gomez, 2012). Therefore, this approach
cannot be considered as a plug and play methodology (as in the
case of working with digital photographs) with regard to high
unpredictability of obtained results (Bakker, Lane, 2017).
Working with archival datasets requires the thorough
understanding and careful application of these software (Bakker,
Lane, 2016).
Different principal point position in each photo, not the same
resolution and a photo frame with fiducial marks are main
problems in case of using scanned, aerial photographs in SfM
technology. The solution to the aforementioned problems caused
by the specific character of scanned aerial photographs is data
pre-processing. A two-stage approach to archival photographs
orientation offered by Goncalves (2016) consists in taking
advantage of SfM algorithm after all the scanned photographs
have been transformed into the same size and the same position
with respect to the photographic coordinate system, with the use
of affine transformation. In this method, measurement of fiducial
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
197
marks in the scanned photographs was manual which
considerably extends the processing time. The author himself,
however, sees potential for application in the offered approach.
Integration of photogrammetric methods and algorithms known
from Computer Vision (Schindler, 2012) makes automation of
the whole process of adapting scanned aerial photographs to
software based on SfM technology, in order to use their full
potential of these data, possible.
2. SAPC APPLICATION DESCRIPTION
The author's open-source SAPC software (Scanned Aerial
Photographs Correction) (Figure 1A) is designed to transform
scanned aerial analogue photographs to the form with specificity
similar to digital images. Its main task is data pre-processing
thanks to which, through the process of self-calibration, a set of
scanned photographs is treated as being taken with the same
camera (as it has happened in reality).
SAPC does not require any a priori information from a camera
calibration report, such as photo-coordinates for fiducial marks
or a principal point. Application does not determine interior
orientation of camera. Python 3.5 programming language and
OpenCV 3.1 library were used to develop processing that
includes i.e. template matching and affine transformation of
scanned images. Images, which are created in the applied
program as output data, are characterized by the same principal
point position in each photo (that is, its pixel coordinates are
constant) and the same resolution through cutting out the black
photo frame. Additionally, SAPC generates a binary image file,
which can mask areas of fiducial marks. Content of each
photography remains unaffected.
The first step of the technological process of archival
photographs processing in SfM software products is using SAPC
application data pre-processing before camera self-calibration
and aerotriangulation processes are applied (Figure 1B).
(A)
(B)
Scanned analogue
photographs
pre-processing in
SAPC
Import pre-processed
data into SfM
software
Camera
self-calibration and
aerotriangulation
Quality control
↓ ↓
Orthomosaic
Figure 1. A) GUI window of SAPC application
B) Diagram of the technological process for the two-stage
approach to scanned, analogue photographs processing in SfM
software products.
2.1 Workflow
Figure 2 presents a schematic description of SAPC application
workflow:
Importing data (TIFF files)
Template selection (fiducial mark
area) by user
Fiducial mark measurement
Template matching (automatic
fiducial marks detection)
Accuracy analysis of template
matching
Affine transformation / Saving
generated processed photographs
Mask generator (editor and export)
Figure 2. SAPC application workflow
In the first stage, the application user's task is to find and indicate
a fiducial mark, which is clearly seen on a black frame within the
scanned photograph. The indicated part of the image will be
treated as a template at further stages. SAPC software can only
detect optical fiducial marks correctly (not mechanical fiducial
marks) (Figure 3).
Figure 3. Examples of optical fiducial marks, which can be
automatically detected in SAPC
SAPC software limits itself to measuring four corner fiducial
marks, regardless of their number in the image. Determined pixel
coordinates of the four fiducial marks provide redundancy in the
target image affine transformation.
Template matching in SAPC software is done with the
normalized cross-correlation method (OpenCV, 2011). A match
metric between template image (fiducial mark area) (T) and the
source image (scanned photograph) (I) is calculated according to
Equation 1 based on the correlation coefficient:
(1
)
where T(x’,y’) = pixel value in the template image
I(x+x’,y+y’) = pixel value in the source image (in which
we expect to find a fiducial mark)
R(x,y) = pixel value in the result image (which contains
the match metric for each location (x,y))
For each location, there is a value of the match metric concerning
the images determined and stored in the result image (R). This
image can be treated as the so-called matching quality map.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
198
Template matching takes place where the global maximum of
a function R(x,y) occurs.
The next stage involves the accuracy analysis of the conducted
detection of fiducial marks in photographs and image geometric
transformation. These require certain theoretical assumptions
(Figure 4):
- all the calculations are done in a pixel coordinates system;
- fiducial marks are numbered clockwise;
- distances dij and diagonals pij are calculated on the basis of the
pixel coordinates determined in the template matching process;
- corresponding distances and diagonals in the subsequent
photographs are of the same length (e.g. expected value of d12
distance is arithmetic mean of the calculated d12 values in all
photographs);
- fiducial marks coordinates determined in the template matching
process are treated as coordinates in the primary system (different
for each photograph);
- coordinates corresponding with subsequent fiducial marks in
the secondary system (constant for each photograph) are
determined on the basis of linear section based on dij and pij mean
values.
Figure 4. Illustration showing the theoretical assumptions of
SAPC software
The main goal of SAPC software is transforming input scanned
analogue photographs into the form in which the principle point
in each image occurs in the same place; that is, its pixel
coordinates are constant. Therefore, transforming all the rasters
to the same position with respect to the photographic coordinate
system is necessary. With regard to possible non-
perpendicularity of scanner system axes or occurrence of errors
caused by a aerial film shrinkage, SAPC software uses affine
transformation taking the effects of these phenomena into
account.
After the successful transformation, there are fragments of the
black frame left in the result images, including the range of
fiducial marks. Using the editor, the user can specify the mask
range and export it to a binary image file. In SfM software,
exported file can be applying on each photo in a single, automatic
process.
2.2 Output data
The output data, that is, the cut scanned analogue photographs
transformed to the same position with respect to the photographic
coordinate system are automatically saved in the catalogue after
the application ends the process. The result format of rasters is
TIFF with LZW compression. Additionally, the software
generates a binary image (mask) in PNG format in the directory
path. The exported dataset is fully compatible with SfM software.
Exemplary samples of input and output data were shown in
Figure 5:
Figure 5. Example of input and output SAPC data
3. EXPERIMENTAL SECTION
The main goal of the experiment was to investigate the influence
of SAPC pre-processing of archival aerial photographs on the
accuracy of processing in software products based on SfM
technology. In order to achieve that, there were 22 scanned at 14
µm pixel size, analogue photographs at the scale 1:5000 of
Warsaw, which had been captured in 1986, were used in two
variants: unprocessed and pre-processed in SAPC application.
The process of orientation and generation of point clouds was
carried out using two SfM software products: Agisoft PhotoScan
and Pix4D. Additionally, the obtained results of aerotriangulation
were compared with results obtained from professional
photogrammetric software Trimble Inpho, which is the
standard approach to scanned, analogue photographs processing.
There were no additional information on the camera specification
introduced in the process of aerotriangulation since, in most of
the cases of working with archival photographs, the calibration
report is not available. Only the value of the focal length taken
from photo’s frame was used as a reference value in the analysis
of self-calibration results.
In case of archival photographs orientation; essentially. there is a
problem with GCPs. Coordinates of these points are hardly ever
provided together with scanned, aerial photographs. Therefore,
three variants of control/check points distribution (Figure 6) were
analysed, based on 21 points (unchanged over time), which were
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
199
measured with RTK technology for the purposes of the
experiment:
- 1st variant : 11 Controls points, 10 Check points - a very
optimistic variant, Control points distributed evenly on the whole
block of photographs;
- 2nd variant : 5 Controls points, 16 Check points - Control points
at the corners of the block, additional Control point in the centre
of the block;
- 3rd variant : 3 Controls points, 18 Check points - the minimum
number of Control points, enabling one to rescale and orient the
block of photographs in the certain coordinate system.
The measurement of photo points in photographs was done once
using AgiSoft software, and, subsequently, the measurements
were exported to a XML file. In Pix4D, the XML file was
imported so that the influence of the differences of photo point
indications on aerotriangulation was avoided.
Apart from the comparison of deviations at Check and Control
points, the self-calibration results and exterior orientation
elements of the photographs were also analysed. Additionally,
geometric accuracy of point clouds generated in several variants
was investigated.
4. RESULTS
4.1 The influence of GCPs distribution on results of
aerotriangulation with unprocessed and pre-processed data
In all the used software: Agisoft PhotoScan, Pix4D and Trimble
Inpho, in aerotriangulation process, the same statistical weights
were assigned to GCPs: X/Y accuracy - 0.1 m, Z accuracy - 0.2
m. Thanks to assuming higher accuracy values than accuracy of
measurement with the use of the RTK technique the impact of
the following phenomenon was reduced. GCPs were the objects
that have not been changed in the past 30 years. However, in
reality, these might have been modernised during this period but
it could not be verified correctly on the basis of aerial
photographs solely.
Table 1 below shows the results of aerotriangulation of
photographs carried out with the use of Agisoft software,
assuming two variants of photographs: unprocessed and pre-
processed in SAPC, and three variants of GCPs distribution.
Additionally, the table lists reference results obtained in Trimble
Inpho.
Agisoft PhotoScan
Inpho
(Reference)
unprocessed
pre-processed
in SAPC
unprocessed
RMS Error [m]
RMS Error [m]
RMS Error [m]
variant
of
GCPs
X
Y
Z
X
Y
Z
X
Y
Z
1st
Control
Control
Control
0.200
0.143
0.138
0.082
0.092
0.131
0.077
0.106
0.130
Check
Check
Check
0.200
0.181
0.347
0.125
0.095
0.171
0.086
0.063
0.149
2nd
Control
Control
Control
0.158
0.080
0.177
0.083
0.058
0.067
0.042
0.040
0.038
Check
Check
Check
0.201
0.135
0.595
0.156
0.105
0.188
0.135
0.151
0.229
3rd
Control
Control
Control
0.147
0.105
0.010
0.086
0.052
0.008
0.022
0.041
0.011
Check
Check
Check
0.308
0.406
1.210
0.166
0.097
0.358
0.115
0.090
0.237
Table 1. Aerotriangulation results in Agisoft PhotoScan
Figure 6. Visualization of three variants of Control/Check points distribution
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
200
When analysing the aforementioned table, one can notice that in
every case, RMS Errors at Check points are higher for the
unprocessed photographs. Together with a decrease in a number
of Control points (3rd variant of GCPs), horizontal accuracy is
twice higher and vertical accuracy even three times higher for
pre-processed in SAPC photographs. Over one metre value of
RMS Z for unprocessed photographs is unacceptable.
A clear relation between GCPs distribution and RMSE values at
Check points was observed. Additionally, in the first two GCPs
variants, the results obtained from pre-processed data are
comparable with the reference results from Inpho.
Table 2 lists the results of aerotriangulation in Pix4D, carried out
in the variants analogous to the previous case:
Pix4D
Inpho
(Reference)
unprocessed
pre-processed
in SAPC
unprocessed
RMS Error [m]
RMS Error [m]
RMS Error [m]
variant
of
GCPs
X
Y
Z
X
Y
Z
X
Y
Z
1st
Control
Control
Control
0.067
0.083
0.082
0.057
0.072
0.086
0.077
0.106
0.130
Check
Check
Check
0.149
0.067
0.255
0.124
0.072
0.183
0.086
0.063
0.149
2nd
Control
Control
Control
0.026
0.027
0.062
0.032
0.030
0.066
0.042
0.040
0.038
Check
Check
Check
0.171
0.137
0.289
0.161
0.127
0.298
0.135
0.151
0.229
3rd
Control
Control
Control
0.002
0.001
0.001
0.001
0.001
0.001
0.022
0.041
0.011
Check
Check
Check
0.135
0.239
0.618
0.164
0.109
0.212
0.115
0.090
0.237
Table 2. Aerotriangulation results in Pix4D
In 1st and 2nd variant of GCPs, the were no considerable
differences in RMS Errors at Control and Check points between
unprocessed and pre-processed in SAPC photographs. Only in
the case of a limited number and unfavorable distribution of
GCPs (3rd variant), vertical accuracy shows clear difference to
pre-processed photographs' advantage.
In case of pre-processed photographs, the obtained results of
aerotriangulation in all variants of GCPs were comparable to
Trimble Inpho reference results.
When comparing the results from Table 1 and Table 2, one can
notice that Pix4D manages the case of a limited number of
Control points, which are used to georeference a spatial model to
the ground, better. Both for unprocessed and pre-processed
photographs, in the third GCPs variant, vertical accuracy for
Pix4D is twice higher than for Agisoft. However, using SAPC
prior to taking advantage of both software products has a positive
influence on the final results of aerotriangulation.
4.2 Camera self-calibration
The clearly visible Z-accuracy difference between a few variants
of aerotriangulation is caused by results of self-calibration. The
below table (Table 3) shows determined ck value for a block of
photographs unprocessed and pre-processed in SAPC in the 3rd
variant of GCPs, for which the difference of aerotriangulation
accuracy was the most visible. Reference value ckref = 213.75
mm was taken from the photo’s frame. Value Δcki was
determined according to Equation 2:
Δcki = ckref - cki
(2)
where ckref = ck reference value taken from the photo’s frame
cki = ck value from i-th set of parameters determined
in the process of self-calibration
Agisoft PhotoScan
Pix4D
unprocessed
unprocessed
i
cki [pix]
cki
[mm]
Δcki
[mm]
cki
[pix]
cki
[mm]
Δcki
[mm]
1
15618.2
218.65
-4.90
15288.8
214.04
-0.29
2
15532.9
217.46
-3.71
15189.1
212.64
1.10
3
15394.7
215.52
-1.77
15239.8
213.35
0.39
4
15483.7
216.77
-3.02
15153.5
212.14
1.60
5
15702.0
219.82
-6.07
15178.3
212.49
1.25
6
16366.0
229.12
-15.37
15165.4
212.31
1.43
7
15618.6
218.66
-4.91
15169.4
212.37
1.37
pre-processed in SAPC
pre-processed in SAPC
i
cki [pix]
cki
[mm]
Δcki
[mm]
cki
[pix]
cki
[mm]
Δcki
[mm]
1
15320.7
214.49
-0.74
15275.8
213.86
-0.11
Table 3. Camera self-calibration results in both SfM software
products: Agisoft PhotoScan and Pix4D.
Regarding unprocessed photographs, several sets of interior
orientation were calculated by self-calibration algorithm, which
assumes that these photos hadn’t been captured by the same
camera (not the same resolution of each photo). On the other
hand, only one set of camera calibration parameters was
calculated for the images pre-processed in SAPC. The same
phenomenon was observed in both programmes: Agisoft
PhotoScan and Pix4d.
The number of the sets of parameters determined in the process
of self-calibration would not be so alarming if their results were
similar. Unfortunately, for the unprocessed photographs,
determined cki values do not fluctuate around ckref reference
value. For Agisoft Δcki it is even up to -15 mm, while for Pix4D
it is up to 1.6 mm. Discrepancy of the self-calibration results
within the block of photographs taken with the same camera has
a direct impact on the results of aerotriangulation.
Essentially, Δcki is lower by an order of magnitude for both
software products for pre-processed photographs. Better self-
calibration results were obtained in Pix4D than in Agisoft
PhotoScan.
4.3 Comparison of exterior orientation
Additionally, apart from the direct analysis of RMSE at
Control/Check points, the results of the photographs' exterior
orientation elements determined in SfM software for 1st and 3rd
variant of GPCs distribution were compared with the reference
ones obtained in Trimble Inhpo. RMS Error for each of the 6
exterior orientation elements was calculated on the basis of the
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
201
results of aerotriangulation carried out in Agisoft Photoscan
(Table 4):
Agisoft PhotoScan
variant of
GCPs
1st
3rd
Source
RMS
Error
unprocessed
pre-
processed
in SAPC
unprocessed
pre-
processed
in SAPC
X [m]
1.792
2.062
3.396
2.505
Y [m]
26.140
1.219
8.711
1.466
Z [m]
0.720
0.744
37.137
1.310
Omega [°]
0.0942
0.1313
0.1081
0.1398
Phi [°]
0.0965
0.0687
0.0755
0.0637
Kappa [°]
0.3264
0.0278
0.3307
0.0265
Table 4. Results of the assessment of exterior orientation
accuracy determined in Agisoft for the block of photographs
unprocessed and pre-processed in SAPC
When analysing the aforementioned table, one can state that a
high value of RMS Error Y for the unprocessed photographs in
the 1st variant of GCPs is puzzling. In the analysed block, Y-axis
was related to forward overlap of the photographs. RMSE values
for the rest of the positional elements of exterior orientation are
similar to the values obtained on the basis of the photographs pre-
processed in SAPC. In the case of angular values, the fact that
RMS Error for Kappa angle is lower by an order of magnitude
for pre-processed data is worth mentioning.
In the case of the 3rd variant of GCPs, RMS Error Z was as many
as 37 metres for the unprocessed photographs. Therefore, one can
state that SfM algorithm determined the location of the
perspective centres at a completely wrong height. The reason for
such results was, most of all, the aforementioned camera's self-
calibration error. For photographs pre-processed in SAPC, RMS
Error for all the positional elements of exterior orientation is
lower, and RMS Error Kappa is lower by an order of magnitude
compared to the unprocessed.
Analogously, an analysis was conducted for the results of exterior
orientation obtained in Pix4D (Table 5). In the case of results
from Pix4D, RMS Error for all the positional elements of exterior
orientation is lower for pre-processed photographs in both GCPs
variants. One should especially focus here on RMSE Z, which
reaches several (1st variant), or even several dozen (3rd variant)
lower value for pre-processed data. What is more, RMSE for
angular values also look promising. Data pre-processing in SAPC
resulted in a clear improvement of results of spatial orientation of
the photographs.
The scale of the described problem with the unprocessed
photograph exterior orientation is well presented by the below
visualisation from AgiSoft (Figure 5). The perspective centres of
the unprocessed photos (Figure 5A) are not at similar flying
height as opposed to the photographs pre-processed in SAPC
(Figure 5B). Knowing the rules of photogrammetric project
planning, the results of exterior orientation for the unprocessed
photographs cannot be considered correct.
Pix4D
variant of
GCPs
1st
3rd
Source
RMS
Error
unprocessed
pre-
processed
in SAPC
unprocessed
pre-
processed
in SAPC
X [m]
1.563
1.241
1.528
1.903
Y [m]
6.008
1.877
6.621
1.424
Z [m]
2.248
0.672
3.632
0.121
Omega [°]
0.0867
0.0919
0.0423
0.0205
Phi [°]
0.1035
0.0355
0.048
0.016
Kappa [°]
0.3182
0.0256
0.3193
0.0035
Table 5. The results of assessment of exterior orientation
accuracy determined in Pix4D for the block of photographs
unprocessed and pre-processed in SAPC
A)
B)
Figure 5. An example of an adjusted single strip from block of
scanned, analogue photographs: A) unprocessed;
B) pre-processed in SAPC application.
4.4 Geometric accuracy of point clouds
In most of the cases of working with archival photographs,
aerotriangulation is not the final stage of processing. Photographs
alignment is only necessary to generate derivative products, that
is, a point cloud, orthophotomap or DSM. Below, there is the
analysis of the influence of an increase in accuracy of
aerotriangulation through pre-processing in SAPC application on
geometric accuracy of point clouds generated from the block of
the scanned analogue photographs.
The analysis used point cloud generated by image matching:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
202
- in Agisoft PhotoScan, using unprocessed archival photographs
in I and III variant of GCPs distribution;
- in Agisoft PhotoScan, using archival photographs pre-
processed in SAPC in I and III variant of GCPs distribution;
- in Pix4D, using unprocessed archival photographs in I and III
variant of GCPs distribution;
- in Pix4D, using archival photographs pre-processed in SAPC
in I and III variant of GCPs distribution.
The analysis included vertical accuracy of the generated point
clouds. Reference data was LIDAR point cloud, obtained for the
area of Warsaw in 2011. During 25 years, the land cover for the
analysed area has changed radically. Therefore, it was necessary
to determine the objects which have not changed over time. In
order to achieve that, there was a mask created for roof coverings,
parking lots, playing grounds, which heights have remained the
same over the decades.
The comparison of the whole point clouds created from two
extremely different data sources: image-matching and LIDAR
might have been difficult. It was decided that DSMs would be
generated in grid = 50 cm. The created rasters were cut to the area
of the previously prepared mask. The statistical values of Mean
and Standard deviation (STD) were calculated on the basis of
differential rasters calculated according to Equation 3:
DSMdifferential = DSMLIDAR DSMimage-matching
(3)
where DSMdifferential = differential DSM raster
DSMLIDAR = DSM generated on the basis of LIDAR
point cloud
DSMimage-matching = DSM generated on the basis of the
point cloud from image-matching
Agisoft
PhotoScan
Pix4D
DSMdifferential
DSMdifferential
variant
of
GCPs
Source
Statistical
parameter
unprocessed
pre-
processed in
SAPC
unprocessed
pre-
processed in
SAPC
1st
Mean [m]
0.071
-0.003
-0.061
0.015
STD [m]
0.514
0.356
0.843
0.619
3rd
Mean [m]
-1.095
-0.070
0.544
-0.018
STD [m]
0.912
0.564
4.840
1.121
Table 6. The results of the assessment of photograph exterior
orientation accuracy determined in Pix4D for the block of
photographs unprocessed and pre-processed in SAPC
In the case of 1st variants of GCPs for both software products,
differences of Mean and STD values between DSMdifferential from
photographs unprocessed and pre-processed in SAPC are not
significant. In the situation when a number of GCPs is limited
(3rd variant), differences in the values of statistical parameters
are clear. In the case of AgiSoft, Mean value = -1.095 m / STD =
0.912 m for unprocessed photographs shows that the whole
DSMmage-matching has assigned wrong elevation values towards
DSMLIDAR. Therefore, one can state that the source image-
matching point cloud contains a considerable number of
elevation modelling errors. For Pix4D, analogously to the
previous case, Mean value of raster DSMdifferentia for unprocessed
data, was over 0.5 m, while STD almost 5 metres. The high value
of the second parameter shows errors in elevation modelling as
well as the occurrence of much noise in the source point cloud.
It might seem that in the situation of having a large number of
GCPs evenly distributed in the block, photographs pre-
processing in SAPC software is groundless. However, despite the
analysis of geometric accuracy of a point cloud, one should also
scrutinize the completeness of the created model. The figure
below (Figure 6) presents visualisations of point clouds (top
view) generated in Pix4D in several variants:
Figure 6. Top view of point cloud generated by image matching
in Pix4D using:
A) unprocessed archival photographs in 1st variant of GCPs;
B) pre-processed in SAPC archival photographs in 1st variant of
GCPs;
C) unprocessed archival photographs in 3rd variant of GCPs;
D) pre-processed in SAPC archival photographs in 3rd variant
of GCPs.
The above figure shows a clear difference in completeness of data
between point clouds in Figure 6A and Figure 6B. Problems with
spatial orientation of photographs and wrong results of self-
calibration caused holes in the point cloud of Figure 6A in areas
where the actual overlap between photos in the block does occur.
Additionally, there is much noise visible on the edges of the
block. The point cloud created from the photographs pre-
processed in SAPC (Figure 6B) does not have clear data holes
and seems to be complete.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-1/W1, 2017
ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
203
In case of 3rd variant of GCPs, numerous data holes are visible
for both point clouds (Figure 6C and 6D); however, there are far
too many for the cloud of points generated from the unprocessed
photographs.
When comparing point clouds from Figure 6A and Figure 6D one
may draw a conclusion that data pre-processing in SAPC has a
greater impact on the quality of the resulting model than
increasing GCPs density and generating point clouds from
unprocessed photographs.
5. DISCUSSION
SAPC application seems to be a useful tool when working with
archival aerial photographs, as this was confirmed by the results
of the conducted experiment. In the case of limited access to
GCPs, obtaining acceptable results of aerotriangulation in SfM
software products on the basis of unprocessed data is very
difficult. Thanks to using pre-processing in SAPC, the problems
with camera self-calibration and photograph spatial orientation
do not occur. This has a direct influence on the obtained level of
processing accuracy - comparable with reference results obtained
in the professional photogrammetric system. Additionally,
a close relationship between accuracy of photographs' block's
orientation and data completeness in the resulting derivative
product is visible. Better results of aerotriangulation determine
a higher quality of the generated model.
On the other hand, apart from accuracy aspects, working with the
use of SfM software on archival data earlier processed in SAPC,
is much nicer and less time-consuming. Unprocessed photos are
characterized by varied resolution, so applying masks on each
photo manually is necessary. In case of SAPC images, this
awkward action can be substituted by a single, automatic process.
Moreover, results of initial alignment (without GCPs) for pre-
processed photos are much better than for unprocessed ones.
Thereby, when GCP was placed in two photos, SfM software
suggests its location in images far more accurately.
6. CONCLUSION
SfM software producers are not planning to implement the
modules designed for archival data in the near future due to
relatively low percentage of users interested in these add-ons.
Treating scanned analogue photographs as photographs taken
digitally clearly facilitates and speeds up the process of
orientation. The presented SAPC application automatically
provides proper processing of archival data therefore enabling
one to take advantage of the full potential of photographs in
photogrammetric processing with the use of software based on
SfM. Described two-stage approach: pre-proccessing in SAPC
and aerotriangulation in SfM software, may be an alternative to
professional photogrammetric systems, essentially in case of lack
of required data about a camera and approximate values of
exterior orientation parameters. Apart from the economic issues,
this approach seems to be more friendly and easier for users, who
are not associated with photogrammetry in their daily works, and
who are using archival photographs more and more willingly.
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ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany
This contribution has been peer-reviewed.
doi:10.5194/isprs-archives-XLII-1-W1-197-2017
204

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Climate records show that the Antarctic Peninsula is rapidly warming. Dramatic changes in ice shelf and glacier extent have been recorded over the last few decades. Mapping recent changes in the Antarctic Peninsula is relatively straightforward, as increased amounts of earth observation data become readily available for scientific purposes. However, long term measurements of volumetric changes within the region are rare and less is known of changes which have occurred over the second half of the 20th Century. Nonetheless, historical observations are available in the form of archival aerial stereo-photography. However, extracting information from historical data, i.e. to compare it to recent data, is not trivial, and to-date, this data source remains largely untapped, despite its rich potential. Often such imagery is stored in non-digital format and may have degraded over time. Other problems relate to insufficient metadata or ground control. Typically these difficulties result in poor registration of multi-temporal DEMs, which degrade subsequent measurements of surface change. This is one of the fundamental limitations of accessing archival datasets. In this research a least squares surface matching technique is introduced to overcome these challenges and achieve reliable registration of multi-temporal DEMs. Historical imagery acquired in the 1960s for two Antarctic Peninsula glaciers is processed to extract DEMs, which are subsequently compared to DEMs derived from modern ASTER satellite data and aerial photography. Through the surface matching approach, it is shown that the registration accuracy of the historical and modern-day datasets can be improved significantly. This enables precise quantification of glacier elevation changes on a multi-decadal time scale. Frontal glacier surface lowering of up to 50 m was observed over the last ~4 decades. Results of this study allow a better understanding of historical volumetric glacier changes of the Antarctic Peninsula and provide an efficient and automated method for improved DEM co-registration.
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This article presents a 4D modelling approach that employs multi-temporal and historical aerial images to derive spatio-temporal information for scenes and landscapes. Such imagery represent a unique data source, which combined with photo interpretation and reality-based 3D reconstruction techniques, can offer a more complete modelling procedure because it adds the fourth dimension of time to 3D geometrical representation and thus, allows urban planners, historians, and others to identify, describe, and analyse changes in individual scenes and buildings as well as across landscapes. Particularly important to this approach are historical aerial photos, which provide data about the past that can be collected, processed, and then integrated as a database. The proposed methodology employs both historical (1945) and more recent (1973 and 2000s) aerial images from the Trentino region in North-eastern Italy in order to create a multi-temporal database of information to assist researchers in many disciplines such as topographic mapping, geology, geography, architecture, and archaeology as they work to reconstruct building phases and to understand landscape transformations (Fig. 1). (Figure Presented)
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The acquisition of topographic data is crucial for earth sciences, especially for the monitoring of topographiy that may change drastically in a short-time such as volcanoes. Precise topographic data are usually acquired using Lidar technologies, both airborne and terrestrial, but this method tends to be very costly both financially and in term of time for data processing. The present article present therefore an alternative technique, which has been only described once in a earth-sciences article: Structure from Motion (SfM) combined with Multiple View Stereophotogrammetry (MVS). SfM-MVS is a technique developed in computer vision that allows the calculation at the same time of both the position of the camera and the reconstruction of the 3D of a scene, and then recreate a 3D mesh of the topography. The present article is testing the technique at the Iwaki volcano in Northern Honshu in Japan, using a series of historical aerial photographs taken in 1965, 1985 and 1994. This has allowed the reconstruction of the topography and the drawing of orthophotographs, but comparison with data provided by topographic data have shown that the accuracy of the topographic results tends to reduce at the external limits of the volcanoes, most certainly because of the lack of images to accurately calculate the elevation. In order to confirm the validity of the technique and to show that the lack of accuracy is linked to the character of the data, a laboratory experiment using a decimeter scale sample was conducted. The results of this experiments showed excellent and accurate results with 3D points characterized by a correlation coefficient R 2 or 0.9985 between the calculated and the measured data. This demonstrates the possibility of such technique, not only in earth sciences, but also archaeology, space exploration, etc.
Structure from Motion (SfM) photogrammetry applied to historical imagery: plug & play?
  • M Baaker
  • S N Lane
Baaker, M., Lane, S. N., 2017. Structure from Motion (SfM) photogrammetry applied to historical imagery: plug & play? Geophysical Research Abstracts, Vol. 19, EGU2017-10446, EGU General Assembly 2017.
Archival photogrammetric analysis of river-floodplain systems using Structure from Motion (SfM) methods
  • M Baaker
  • S N Lane
Baaker, M., Lane, S. N., 2016. Archival photogrammetric analysis of river-floodplain systems using Structure from Motion (SfM) methods. Earth Surface Processes and Landforms (2016).
OpenCV library documentation
  • Opencv
OpenCV, 2011. OpenCV library documentation: http://docs.opencv.org/3.0-beta/index.html# (Visited in March 2017).
Developments in Computer Vision and its Relation to Photogrammetry
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Schindler, K., 2012. Developments in Computer Vision and its Relation to Photogrammetry. Invited paper of XXII ISPRS Congress, Melbourne, Australia.