# The Time-SIFT method : detecting 3-D changes from archival photogrammetric
analysis with almost exclusively image information

D. Feurer<EMAIL_ADDRESS>F. Vinatier  LISAH, Univ Montpellier, INRA,
IRD, Montpellier SupAgro, Montpellier, France

###### Abstract

Archival aerial imagery is a source of worldwide very high resolution data for
documenting paste 3-D changes. However, external information is required so
that accurate 3-D models can be computed from archival aerial imagery. In this
research, we propose and test a new method, termed Time-SIFT (Scale Invariant
Feature Transform), which allows for computing coherent multi-temporal Digital
Elevation Models (DEMs) with almost exclusively image information. This method
is based on the invariance properties of the SIFT-like methods which are at
the root of the Structure from Motion (SfM) algorithms. On a test site of 170
km2, we applied SfM algorithms to a unique image block with all the images of
four different dates covering forty years. We compared this method to more
classical methods based on the use of affordable additional data such as
ground control points collected in recent orthophotos. We did extensive tests
to determine which processing choices were most impacting on the final result.
With these tests, we aimed at evaluating the potential of the proposed Time-
SIFT method for the detection and mapping of 3-D changes. Our study showed
that the Time-SIFT method was the prime criteria that allowed for computing
informative DEMs of difference with almost exclusively image information and
limited photogrammetric expertise and human intervention. Due to the fact that
the proposed Time-SIFT method can be automatically applied with exclusively
image information, our results pave the way to a systematic processing of the
archival aerial imagery on very large spatio-temporal windows, and should
hence greatly help the unlocking of archival aerial imagery for the
documenting of past 3-D changes.

###### keywords:

Method , Automation , Multitemporal DEMs , Photogrammetry , Analog imagery ,
3-D Change Detection

##  1 INTRODUCTION

For decades, aerial imagery was used to produce large-scale geographic and
topographic maps. With - most often - sub-metric resolutions, archival aerial
imagery has hence a remarkable coverage both in the temporal and spatial
dimensions. Archival aerial imagery exists in almost every country in the
world from the first half of the twentieth century (Cowley and Stichelbaut,
2012). This imagery was most often acquired with stereoscopic coverage, which
results in a unequalled potential for 3-D documentation of past changes.
Besides, during the last decade 3-D past changes have been studied using
archival aerial imagery in the disciplines of archaeology (Verhoeven, 2011;
Sevara, 2013; Verhoeven and Vermeulen, 2016; Salach, 2017; Sevara et al.,
2017), geomorphology (Gomez et al., 2015; Gonçalves, 2016; Ishiguro et al.,
2016; Bakker and Lane, 2017), glaciology (Mertes et al., 2017; Mölg and Bolch,
2017; Vargo et al., 2017), and volcanology (Gomez, 2014).

However, the full potential of this huge volume of historical data may have
not been widely exploited yet. This is mainly due to the fact that accurate
processing of historical aerial imagery time series for 3-D change assessment
requires other information than the images themselves. Indeed, external
information such as camera calibration certificates, ground control points
(GCPs), or the a priori knowledge of stable zones is needed to estimate
exterior and interior orientation of the image blocks. This is all the more
important when the aim is to compute Digital Elevation Models (DEMs) of
differences (abbreviated into DoD by Lane et al. (2003); Wheaton et al.
(2010); Williams (2012)), which require that differentiated DEMs are both
accurate and spatially consistent between each other.

Historically, the construction of 3-D models began with photogrammetry,
firstly with stereoscopic analysis of oriented image pairs and then with
automatic correlation of oriented images (see for example Kraus and Waldhäusl,
1998). Using these ’classical’ photogrammetric methods, there are several
examples of authors succeeding in differencing DEMs within most favourable
conditions, i.e when image datasets are associated with all necessary
calibration certificates (Fabris and Pesci, 2005; Fischer et al., 2011;
Micheletti et al., 2015; Aucelli et al., 2016; Fieber et al., 2018). Having
calibration certificates is not sufficient, though. Fischer et al. (2011) and
Fieber et al. (2018) \- with a specific case implying DoD with a satellite DEM
for the latter work - needed to proceed additional co-registration of the
photogrammetric DEMs, with a priori knowledge and manual delimitation of
stable areas when necessary. Moreover, the use of ’classical’ photogrammetric
software, requires accurate initial information (interior orientation, ground
control points) so that processing succeeds. For instance, in a work realised
with the ERDAS LPS software, Micheletti et al. (2015) had to give a specific
care to ground control points choice, positioning, distribution and accuracy.

When calibration certificates are missing, which is not rare for oldest
images, interior geometry can be estimated through autocalibration, which
estimates simultaneously interior and exterior orientations. This method was
successfully used by several authors with a view to compute DoDs thereafter
(e.g. Chandler and Cooper, 1989; Walstra et al., 2007; Redweik et al., 2016).
In their pioneering work, Chandler and Cooper (1989) reminded that interior
orientation consists in two groups of parameters: the parameters
characterising the displacement of the principal point relatively to the
fiducial marks, and the parameters characterising the lens distortion. To
these parameters has to be added the deformation due to the scanning, which
has a strong impact on the performance of 3-D estimation (Sevara, 2016). These
additional unknowns severely increase the need for ground control points,
which in turn raises new issues : the need for expensive additional ground
survey and the problem of the accuracy of orientation estimation with self-
calibration algorithms (Aguilar et al., 2013).

A frequent strategy to collect these additional GCPs at limited cost is to
rely on recent data. These are usually of better quality, associated with
existing and accurate calibration information, fiducial marks and/or even
contemporary GCPs. James et al. (2006) extracted different numbers of required
GCPs from a shaded-view of a lidar DEM and then detected 3-D changes from
photogrammetric DEMs estimated with older images. Several authors exploited a
well-known or well-established geometry of recent image blocks, extracted GCPs
from these recent images and used these GCPs in older imagery in order to
determine interior and exterior orientation of these older image blocks
(Hapke, 2005; Dewitte et al., 2008; Zanutta et al., 2006; Fox and Cziferszky,
2008). More recently, Giordano et al. (2018) proposed a method allowing for an
automatic collection of these GCPs. GCPs are detected in a recent orthophoto
and DEM and then transferred to images of previous dates. The method relies on
the detection of keypoints between images of different dates, which is made
possible through a first estimation of coarse DEMs and orthophotos of ancient
dates. These first estimates are done on the basis of available image metadata
at all dates.

The advent of Structure from Motion (SfM) and Multi View Stero (MVS)
algorithms in geoscience and archaeology in the last decade provided a new
paradigm. Indeed, as explained for instance by Westoby et al. (2012) or
Fonstad et al. (2013), SfM algorithms compute relative orientations with image
information exclusively, thanks to keypoints detection algorithms such as the
Scale Invariant Feature Transform (SIFT) (Lowe, 2004). The potential of SfM-
MVS algorithms for 3-D exploitation of archival aerial imagery was detected
very soon by Verhoeven (2011) who succeeded in obtaining a visual 3-D model
from historical aerial imagery with exclusively image information. Moreover,
the use of SfM software usually does not require advanced skills in
photogrammetry, which allowed a wider use of these techniques, as noticed by
recent reviews (Eltner et al., 2016; Smith et al., 2016; Mosbrucker et al.,
2017). However, even if SfM algorithms have allowed for a more accessible and
more straightforward use of archival aerial imagery, it did not eliminate the
need for thorough control of image acquisition geometry for differencing DEMs
computed with such imagery. Bakker and Lane (2017) has noted that SfM-MVS
photogrammetric processing shares the limitations of classical methods : the
propagation of linear errors into the final DEMs requires specific correction
procedures for subsequent accurate DEM differencing. Once again, control of
image and DEM geometry in SfM-based workflows was done through the use of
additional external information. Several authors have used GCPs to help the
autocalibration (Gomez, 2014; Verhoeven and Vermeulen, 2016; Mertes et al.,
2017). Other authors have co-registrated the DEMs obtained from archival
aerial imagery onto reference DEMs, with an a priori knowledge of stable zones
(Bakker and Lane, 2017; Mertes et al., 2017; Mölg and Bolch, 2017; Sevara et
al., 2017).

Finally, as remarked for instance by James et al. (2017) in the context of
unmanned aerial vehicles imagery processing, it is worth noting that quality
and consistency of surveys realised with SfM-MVS algorithms still rely on an
in-depth adjustment of processing parameters. Parameters adjustment may be
even more complex and sensitive when dealing with interior orientation of
archival aerial imagery due to the additional degrees of freedom of the
problem. Thus, and even if SfM-MVS algorithms had made 3-D exploitation of
aerial imagery more widely accessible, processing of archival imagery has
still required manual intervention to avoid gross errors. This has been done
by visual inspection throughout the whole processing (e.g. Bakker and Lane,
2017) or with complex strategies for the choice of parameters (e.g. Verhoeven
and Vermeulen, 2016).

Thus, even if SfM photogrammetry should allow for a broader use of historical
aerial imagery, all existing methods for extracting 3-D changes from this
archive still rely on external data, significant expertise and/or parameters
optimisation. This need for expertise and additional data still impedes the
actual unlocking of big archival aerial imagery datasets. There is hence a
need for a method that would allow the automated production of DoDs from
archival aerial imagery with minimal external data and expertise.

In this context, our study aims at assessing a new method that takes benefit
of the SIFT-like algorithms capabilities for a new purpose. Invariance of
features detected by SIFT-like algorithms is originally spatial. It consists
in invariance to scale and to simple geometric transforms. Our work lies on
the fact that SIFT-like algorithms also show invariance in time, as noticed
for instance by Chanut et al. (2017), and by Vargo et al. (2017). The
principle of our proposed method, hence termed Time-SIFT, is to merge all
dates of a collection of historical image archives in the same SfM processing.
Our work proposes and tests an automatic method where images of different
dates are processed all together in the same block during the SfM step. As a
result, all dates share a same unique geometric reference, which saves the
expensive and/or cumbersome collection of additional external data. Moreover,
putting all images in the same block diminishes the linkage between interior
end exterior orientation unknowns, hence allowing the use of SfM algorithm
with no a priori photogrammetric expertise nor complex strategies for the
choice of SfM parameters.

To the best of our knowledge, there is no existing study about the potential
of a method which would systematically gather images of different dates in the
first steps of SfM algorithms for DEM differencing. The aim of our paper is
hence to assess the potential of the proposed Time-SIFT method to
automatically obtain informative DoDs with limited input from the user, i.e
with almost no other information that image information and with limited
expertise in photogrammetric processing.

In this paper, we propose a test of the different classical processing methods
usually used in the literature as compared with the proposed Time-SIFT method.
3-D models and DoDs are computed with different processing options and their
quality is then assessed with independent DEMs. This analysis allows to
determine the most significant processing options. A visual inspection of
specific 3-D changes detected in the DoDs is also done in order to ascertain
the capacity of the proposed method for mapping past 3-D changes.

##  2 Material and methods

###  2.1 Test site and image data

The test site is a 170 km2 rectangle of a Mediterranean landscape located in
Occitanie in southern France (43°5N, 3°19E). This zone is mainly covered by
vineyards with forests in uppermost parts. This zone exhibited a change in
land management through a severe transformation of vineyards areas during the
1980s, from goblet to trellised vineyards, with a progressive land abandonment
and urbanization in the last 50 years (Vinatier and Arnaiz, 2018). Altitudes
vary between 0 and 350 m (Figure 1). Archival aerial images were recently
released by the IGN, the French national geographic institute and are freely
available on the IGN’s website (https://remonterletemps.ign.fr/). These images
were scanned with photogrammetric scanners, which is important for a further
photogrammetric processing as noted by Sevara (2016) and also observed in a
preliminary study (Feurer et al., 2017). We used a sample of these data at
four different dates allowing for a stereo coverage of the entire test site
(coloured polygons on Figure 1). Characteristics of these images are reported
in Table 1.

Date |  Focal length (mm) |  Estimated flight height (m) |  Estimated scale |  Images (#) |  Scan size ($\mu$m) |  Estimated ground resolution (cm)
---|---|---|---|---|---|---
21-06-1971 |  152 |  2700 |  1/18000 |  61 |  21 |  37
16-06-1981 |  153 |  4800 |  1/32000 |  27 |  21 |  66
25-06-1990 |  153 |  5000 |  1/32000 |  31 |  21 |  69
04-06-2001 |  153 |  4000 |  1/26000 |  44 |  21 |  55
Table 1: Characteristics of image data. Figure 1: Localisation of the test
area, image data, external DEMs and additional points. The coarse DEM used for
validation, represented in colour scale in the middle of figure, covers the
whole test area of 170 km2. The smaller area bordered by dashed lines
corresponds to the second validation DEM, represented with the same color
scale. Black points represent the 200 manually marked points. Points belonging
to the minimal set of 9 manually marked points are circled. Shaded rectangles
highlight the zones where full resolution qualitative analysis of the DoDs was
done. Two perpendicular lines correspond to the North-South and East-West
transects. Approximate footprints of the image blocks are represented by
coloured polygons. Map projection : Lambert 93 (EPSG:2154)

###  2.2 DEMs and DoDs computing

DEM computing was done with AgiSoft Photoscan Pro© 1.2.6, which follows a SfM-
MVS workflow. Processing parameters are reported in Table 2. First, cameras
were aligned. This step begins with an automated image features detection and
matching which is comparable to the SIFT algorithm (Semyonov, 2011). Based on
the automatically matched points between images, image interior and relative
exterior orientations were computed. Then an optimization step where
autocalibration is refined was performed. Depending on the different
processing options described in section 2.3.3 below, additional manually
marked GCPs and/or tie points could be used at this step. Once the image block
geometry was refined, the dense image matching step was done and resulted in
the estimation of a dense 3-D point cloud. Finally, DEMs were exported with a
1 m ground sampling distance with disabled interpolation to avoid subsequent
processing artefacts in the DoDs. DoDs were finally computed by subtracting
two successive DEMs. No-data values in at least one of the two input DEMs
resulted in no-data values in the computed DoD.

Processing step | Property | Value
---|---|---
Alignment | Accuracy | Highest
| Pair preselection | Disabled
| Key point limit | 100,000
| Tie point limit | 50,000
| Adaptive camera model fitting | Yes
Optimization | Lens parameters | f,b1,b2,cex,cy
|  | k1,k2,k3,k4,p1,p2
| Marker accuracy (pix) | 5
| Marker accuracy (m) | 20
| Tie point accuracy (pix) | 1
Dense cloud | Quality | High
| Depth filtering | Moderate
Mesh | Surface type | Arbitrary
| Interpolation | Disabled
| Quality | Medium
| Depth filtering | Moderate
DEM | Pixel size (m) | 1
| Interpolation | Disabled
Table 2: Parameters used in PhotoScan Pro©.

###  2.3 Investigated processing options

The interest of the proposed Time-SIFT method was evaluated by testing
alternative processing options at various steps of the SfM-MVS processing
chain. Apart from the two options of using or not the Time-SIFT method, three
image pre-processing options, three options for the use of additional manually
marked points and two autocalibration options were investigated. Details about
these options is given in the following subsections. These different
processing options resulted in 36 different processing scenarios and hence,
with 4 dates, 144 DEMs and 108 DoDs.

####  2.3.1 Image pre-processing

Converting image coordinates into camera coordinates requires to consider (i)
the film movement in the camera body between two image acquisitions (ii)
possible film deformations during storage and (iii) possible geometric
deformation during film scanning. Fiducial marks are usually used to estimate
the geometric transform between image coordinates and camera coordinates.
Another point, raised by Gomez et al. (2015) and questioned by Bakker and Lane
(2017), is the fact that image borders, where fiducial marks and other
metadata appear, can interfere with the automated feature detection algorithm
used in SfM workflows and may need to be masked.

Three options were hence tested. The first option, namely original, consisted
in using images ’as is’, i.e. without any pre-processing. The second option,
namely cropped, consisted in a simple image crop so that image borders were
removed. The third option, namely ReSampFid, consisted in a resampling of the
scanned images based on the four fiducial marks of the corners so that the
resulting images shared the same geometry, with fiducial marks centres
constituting the corners of the resampled images. The third option was done
with the ReSampFid tool of the MicMac open source photogrammetric suite
(Rupnik et al., 2017) and an ad-hoc tool for automated fiducial marks
detection developed with ImageJ.

####  2.3.2 Time-SIFT

The proposed Time-SIFT method relies on the properties of the SIFT-like
algorithms used in SfM workflows. These algorithms compute keypoints that are
invariant to image rotation and scale and hence robust across a substantial
range of affine distortions, addition of noise and change in illumination
(Lowe, 2004; Semyonov, 2011).

By making the assumption that a sufficient number of keypoints remain
invariant across each time period, the proposed Time-SIFT method brings a
novelty in the photogrammetric processing of multi-temporal datasets. It
consisted in processing, at the very first step, all images in a single block
for the estimation of interior and exterior orientations. Images of different
dates were then separated and the dense matching steps were done with images
of the same date.

####  2.3.3 Use of additional manually marked points

Different numbers and types of additional - relatively to the automatic tie
points - manually marked points were used in the different processing
scenarios, with a minimal set of 9 GCPs in order to scale and orient the
estimated DEMs and DoDs.

The first option, namely 9 GCPs, corresponded to this minimal set of 9
additional manual GCPs. For the second option, namely 200 tie points, we added
manually marked tie points to reach a total of 200 points. For the third
option, namely 200 GCPs, ground coordinates of all the 200 points were also
given and used in the SfM workflow. For all these three options, the manually
marked points were added to the set of automatic tie points.

The additional 200 points consisted in carefully chosen permanent ground
features, for instance road intersections or little rocks, which were manually
pointed in the images. Corners of buildings or big rocks were avoided so that
the less possible variation of the $z$ coordinate would occur around the
chosen points. At first a regular sampling of 200 locations over the whole
image block area was determined and each of the 200 points was chosen within
this regular grid to ensure an homogeneous point density (Figure 1). Each of
the 9 or 200 points was manually pointed in the aerial images of the four
dates. When used as GCP, $(x,y,z)$ ground coordinates were necessary. They
were collected on the french geographic survey portal on which orthorectified
imagery and DEM can be browsed (https://www.geoportail.gouv.fr/). Empirical
observations during the marking of the 200 points in multi-date aerial images
resulted in estimated accuracy values of 20 m for ground accuracy and 5 pixels
for image accuracy (Table 2 above). The total proceeding time for pointing and
assigning coordinates on 200 locations in 163 images was about twenty hours.

####  2.3.4 Autocalibration

The autocalibration method aimed at taking into account - or not - the fact
that all images of a single date were taken by the same camera. The option by
date consisted in using a single camera model for all the images of a given
date whereas the second option by image consisted in using one camera model by
image. In all cases, we kept the parameters suggested by PhotoScan Pro© for
lens distortion estimation (Table 2).

###  2.4 Quality assessment

####  2.4.1 Validation data

Two external DEMs obtained at different dates and with different methods were
used for quantitative independent validation. A photogrammetric survey of the
area realised in 2012 by Topogeodis provided a DEM with a pixel resolution of
5 m (the larger DEM on Figure 1). Its planimetric accuracy is estimated at 50
cm. The altimetric accuracy is estimated at 30 cm in urban zones and at 1 m in
mountainous and woodlands areas. In 2001, a lidar survey was conduced by
Geolas Consulting on a subaera of 7 km2 (the smaller area bordered by dashed
lines in Figure 1). From these data, a DEM with a pixel resolution of 1 m and
an estimated altimetric accuracy of 30 cm was derived.

####  2.4.2 Quantitative indices

The impact of the different processing options on the results was evaluated
with four different metrics. The first metric, ’Image block RMSE’, was based
on the internal coherence of the image block estimated geometry. It consisted
in calculating the global RMSE of the bundle block adjustment. It was computed
for the 144 image blocks. The second metric, ’Coarse DEM error’, was computed
using as reference the 2012 photogrammetric DEM, which covers the whole test
site (Figure 1). The reference DEM and all the 144 SfM-MVS DEMs were first
resampled at 50 m with bilinear interpolation. The ’Coarse DEM error’ was then
computed as the mean absolute difference between simulated and reference DEMs.
This metric was chosen to evaluate whether the computed DEMs of each date were
coherent with the actual topography. This metric was computed at 50 m
resolution considering that potential 3-D changes would be negligible at this
resolution and has hence been computed for each date. The third metric, ’Fine
DEM error’ was computed with the 2001 lidar DEM, which covers a smaller part
of the test site as shown on Figure 1. It aimed at checking whether the fine
scale topography was correctly estimated by the SfM-MVS DEMs. As for the
’Coarse DEM error’, the metric was computed as the mean absolute difference
between simulated and reference DEMs. Considering that, at this scale, 3-D
change could not be neglected, this metric was only evaluated on the 36 DEMs
computed with the images of the year 2001. The fourth metric, ’DoD quality’,
corresponded to the internal quality of the computed DoDs, estimated by the
mean absolute value of the DoD. These metrics are summarised in Table 3.

Name | Unit | Samples |  Description
---|---|---|---
Image block RMSE | pixels | 144 Image blocks |  Bundle block adjustment RMSE
Coarse DEM error | meters | 144 DEMs |  Coarse resolution mean absolute difference between the resampled SfM-MVS DEMs and reference DEM
Fine DEM error | meters | 36 DEMs |  Mean absolute difference between the 2001 SfM-MVS DEM and the fine resolution reference lidar DEM
DOD quality | meters | 108 DoDs |  Mean of the DoD absolute values
Table 3: Metrics used for the evaluation of the different processing options.

For all these metrics, we examined whether the different processing options
described in section 2.3 had a significant impact on the distribution of the
metric or not. Due to strong skew of the metrics distributions, we used the
Kruskal-Wallis test to determine the significance of the observed differences.

####  2.4.3 Qualitative analyses of the DoDs

We visually examined all 108 DoDs at coarse scale to explain the quantitative
results obtained with scalar metrics. We also examined topographic profiles of
the different DEMs to better understand the results obtained on the DoDs.
Theses profiles were extracted on the two transects drawn on Figure 1. The
best DoDs - in the sense of the ’DoD quality’ metric as described above -
obtained with the less external information (only 9 GCPs) were finally
visually examined at full resolution. The detected change signals were
associated to actual 3-D changes that could be confirmed by other evidences,
either external documentation, either image information. Amongst the 3-D
changes detected, we examined 3-D changes linked to anthropogenic activity,
such as urban growth, civil engineering around highway, quarries and
backfills. These analyses aimed at validating the potential of the Time-SIFT
method for detecting and characterising 3-D changes with frugal ground
information.

##  3 Results

###  3.1 Quantitative assessment

90 Image block RMSE (pix) Coarse DEM error (m) Fine DEM error (m) DoD quality
(m) Processing options samples : 144 samples : 144 samples : 36 (2001 images)
samples : 108 median IQR p-value median IQR p-value median IQR p-value median
IQR p-value Image pre-processing original 0.69 0.50 12.5 13.5 10.7 3.1 8.4
28.2 cropped 0.76 0.29 0.036* 23.6 55.1 4.7e-04*** 39.1 67.4 5.9e-04*** 14.2
25.1 0.20 ReSampFid 0.63 0.24 5.7 10.2 2.5 3.3 7.4 24.3 Time-SIFT without 0.76
1.05 2.6e-03** 20.4 29.9 5.0e-08*** 11.2 33.4 0.27 25.0 65.0 7.8e-13*** with
0.64 0.23 7.5 5.2 6.2 5.4 2.1 5.5 Additional manually marked points 9 GCPs
0.68 0.24 9.1 18.1 8.8 12.8 10.1 23.3 200 tie points 0.67 0.26 0.062 9.7 16.6
0.82 9.8 25.7 0.62 11.0 22.8 0.50 200 GCPs 0.74 0.53 12.3 64.1 6.8 27.4 14.4
57.3 Autocalibration by image 0.69 0.27 0.83 11.8 24.9 0.31 8.5 36.5 0.90 12.0
16.8 0.49 by date 0.70 0.30 8.9 26.4 9.7 12.0 14.3 39.7

Table 4: Effect of the processing options on the image block quality, DEM
quality and DoD quality. For each metric, the total sample number is recalled
and the median value, inter-quantile range (IQR) and the p-value of the
Kruskal-Wallis test for the significance of the difference of medians is
given.

The main parameters of the metrics distributions are shown in Table 4. For
more details, distribution boxplots are given in Appendix (Figure 8). These
results exhibit two groups of processing options that had a significant impact
on all metrics: the image pre-processing options and the proposed Time-SIFT
method. The most significant result is that using the Time-SIFT method led to
a median DoD quality value of 2.1 m, within an inter-quartile range of 5.5 m,
compared to a median DoD quality value of 25.0 m (inter-quartile range of 65.0
m) for all the 54 DoDs computed without the Time-SIFT method. For all metrics,
using the Time-SIFT method allowed for achieving a better quality, this result
being significant for 3 metrics over 4. Second, using the ReSampFid option for
image pre-processing also significantly and positively impacted the results by
lowering the Coarse DEM error and Fine DEM error to 5.7 m and 2.5 m,
respectively. Third, and as testified by the negligible significances for this
group of options, adding manually marked points had a negligible impact on the
quality of the DEMs and of the DoDs. Furthermore, results obtained with these
different options were contradictory between metrics. With even worse
p-values, autocalibration options also seemed to have had a negligible impact
on all metrics.

In absolute values, using the Time-SIFT method is also the processing option
which resulted in the best DoD quality, both in terms of median and inter-
quartile range. In addition, the ReSampFid option resulted in the best
individual DEMs both at fine and coarse scale. Our experiments hence showed
that the Time-SIFT method, and secondly the image pre-processing, had the most
significant impact on the DEMs and the DoDs. The ReSampFid option seems most
important to obtain best individual DEMs whereas best DoDs were obtained by
using the Time-SIFT method. This may be due to the fact that accurate DEMs do
not necessarily resulted in accurate DoDs when DEMs geometries were not
coherent enough.

###  3.2 Qualitative assessment

Figure 2 shows the thumbnails of all the 108 DoDs obtained with the different
processing options. First, a few DoDs show spatial patterns that are very far
from what could be expected. These flawed DoDs correspond to situations where
photogrammetric processing converged to an erroneous solution, most likely
since the SfM processing step. For some combinations of processing options,
such as, for instance, using 200 GCPs, not using the Time-SIFT method and
doing autocalibration by date, almost no solution could be found, regardless
of the image pre-processing options.

A strong concave or convex ’doming’ effect (James and Robson, 2014) can be
observed for several DoDs that where computed without the Time-SIFT method.
These findings give indications on the problems that may have occurred during
the SfM processing step, in particular erroneous autocalibration.

Figure 2: DoDs between consecutive periods for each combination of the
processing options. All DoDs are represented with the same colour scale
truncated at $\pm$10 m. Red and blue rectangles framed respectively the best
model without using the Time-SIFT method, and the best model with the Time-
SIFT method.

Figure 3 compares - on two transects - topographic profiles of the best DEMs
computed on the one hand with the Time-SIFT method and on the other hand
without the Time-SIFT method. The DoDs corresponding to these DEMs are marked
respectively by blue and red rectangles in Figure 2. Comparison of theses
profiles shows that the topographic differences between DEMs computed with
different options exhibit low-frequency trends rather than local differences.
Indeed, topographic shapes are locally correctly rendered within all DEMs, but
with large and non-stationary biases in the DEMs computed without the Time-
SIFT method.

Figure 3: Profiles comparison between the bests DEMs computed respectively
with (in black) and without (in grey) the Time-SIFT method. Each line
corresponds to a different date. Left, the East-West transect ; right, the
North-South transect (see transects on Figure 1).

Finally we selected the best DoDs among those obtained by using the Time-SIFT
method and the less additional external information. It corresponded to the
association of ReSampFid, 9 GCPs options and autocalibration by date. On these
DoDs at full resolution, we sought 3-D changes signals and inspected them.

First, we looked for evidences which would demonstrate unambiguously that
these 3-D changes are not processing artefacts. Figure 4 shows excavation
(red) and fill (blue) patterns that are clearly superimposed on the newly
constructed highway. This demonstrates the capability of the detecting 3-D
changes in DoDs computed with the Time-SIFT method and limited external
information. This figure also shows that, even if most of the area contains
3-D difference information, processing may fail. In this zone, problems were
faced in low-texture areas, on vegetation, on some parts of urban areas, and
on some field plots.

Figure 4: 3-D changes associated to highway construction. The 1990-1981 DoD is
superimposed on the 1990 orthophoto. Colourless areas correspond to areas
where no value was computed in the DoD. The DoD was estimated with the Time-
SIFT method, using 9 GCPs, autocalibration with a single camera by date and
the ReSampFid pre-processing.

Figure 5 allows to detail the potential of these DoDs in terms of spatial
resolution. It shows the DoDs thresholded above 2.5 m and superimposed on the
2001 orthophoto. Rectangular patterns in these thresholded DoDs show strong
correlations with the floorspace of the houses that were built within the
period. This figure also shows artefacts which may be either small and
isolated patches, either thin patterns at the edges of sharp objects.

Figure 5: Superimposition of thresholded DoDs (values greater than 2.5 m) onto
the 2001 orthophoto (gray levels). The DoDs were estimated with the Time-SIFT
method, using 9 GCPs, autocalibration with a single camera by date and the
ReSampFid pre-processing. with Time-SIFT.

Figures 6 and 7 allows to detail the potential of the obtained DoDs in terms
of volume quantification. These figures represent respectively the ground
excavating and filling of a limited area. These two 3-D changes couldn’t be
detected from orthophotos alone, in terms of volume quantification and even
for the delineation of the impacted zone. This is especially depicted in
Figure 7, where changes in texture have a larger extent than changes in
surface height.

Figure 6: 3-D changes associated to excavation activities in a quarry. Top :
the 1990 orthophoto. Middle, the 2001-1990 DoD. Colourless areas correspond to
areas where no value was computed in the DoD. The DoD was estimated with the
Time-SIFT method, using 9 GCPs, autocalibration with a single camera by date
and the ReSampFid pre-processing. Bottom : the 2001 orthophoto Figure 7: 3-D
changes associated to ground filling. Top : the 1971 orthophoto. Middle, the
cumulated 2001-1971 DoD. Colourless areas correspond to areas where no value
was computed in the DoD. The DoD was estimated with the Time-SIFT method,
using 9 GCPs, autocalibration with a single camera by date and the ReSampFid
pre-processing. Bottom : the 2001 orthophoto

##  4 Discussion

The objective of this work was to determine the interest of a new method,
called Time-SIFT, to compute informative DEMs of difference with limited
photogrammetric expertise and almost exclusively image information.

After a thorough exploration of different processing strategies, we showed
that the use of the Time-SIFT method allowed for computing DoDs in which
actual 3-D changes could be detected and quantified. Moreover, the Time-SIFT
method played the most significant role to ensure the computation of high-
quality DoDs. To the best of our knowledge, automated computation of multi-
temporal feature points for DoD estimation by feeding SIFT-like algorithms
with multi-temporal datasets was never assessed before. Hapke (2005), Korpela
(2006) and Dewitte et al. (2008) already used multi-temporal tie points for
this purpose but these points were manually marked. Fox and Cziferszky (2008)
computed tie points between images of different dates and even thought about a
method similar to the one proposed in this paper in what they called a
simultaneous ’grand adjustment’. However, and even if these authors
appreciated this possible method as elegant, they rejected this option in
order to keep the quality of the reference block geometry and to avoid
difficult posterior error checks. Thus, and even if temporal SIFT methods are
common in computer vision for the processing of videos, to the best of our
knowledge only two authors had proposed studies with experiments showing some
similarity with our method. In a study aiming at characterising the yearly
evolution of the snowline, Vargo et al. (2017) used SfM photogrammetry to
process a multi-temporal dataset of aerial imagery with at least one image a
year from 1981 to 2017. Due to the fact that for some years only one image was
available, they have had to put images of different dates in the same block.
Nevertheless, they also have had to mask snow and ice out of all images so
that the algorithm could estimate the orientations of these isolated images.
Chanut et al. (2017) also successfully used a SfM algorithm to compute tie
points between images of different dates in a study that aimed at quantifying
3-D movements on a landslide. Multi-temporal tie points were used to
initialise a dense correlation step. Mass movements were finally quantified
from the results of the dense correlation.

The second most important finding is that pre-processing of scanned analogue
imagery also plays a significant role in the processing steps of SfM-MVS. This
is coherent with the existing literature that dealt with the use of archival
aerial imagery with SfM algorithms. Salach (2017) developed a specific
software to resample scanned analogue photographs relatively to information
given by fiducial marks. These authors tested different processing strategies
: with or without pre-processing, and different GCPs number and
configurations. They reported that initial estimates of image orientations
with this pre-processing were better than those obtained without this pre-
processing. More, results with pre-processing and a minimal GCP set were
similar to the results without pre-processing but with a complete set of GCPs.
The latter result is hence corroborated by the results of our study. Gonçalves
(2016) used a manual affine transformation computed on the position of the
fiducial marks to register the scanned analogue images between each other.
This allowed the use of archival aerial imagery in SfM workflow for automated
mosaicing and DEM production. The RMSE of the obtained DEM, of 4.7 m, is of
the same order of magnitude than the DEM errors estimated in our study.
Nocerino et al. (2012) also exploited the fiducial marks to settle an image
reference coordinate system at the geometrical center of the fiducial marks.
RMSE estimated on the $z$ coordinates of check points ranged from 7 m to 10 m.
Our results compares favourably with this estimated decametric accuracy.

The third finding can be seen as more unexpected : our experiments showed that
the use of a relatively high number of added manual GCPs and/or tie points
seems to play a negligible role on the image blocks, DEMs, and DoDs quality.
This finding seems to be in contradiction with the results of previous studies
based on the thorough use of GCPs to compute DoDs from archival aerial
imagery. Indeed, Hapke (2005); Zanutta et al. (2006); Dewitte et al. (2008);
Fox and Cziferszky (2008); Micheletti et al. (2015); Papworth et al. (2016);
Mertes et al. (2017) successfully used GCPs of different sources to ensure a
geometrical consistence between DEMs before subtracting them to each other.
This apparent contradiction may be explained by the fact that these studies
used a method that significantly differs from ours. Contrarily to these
studies, we did a fully automatic photogrammetric processing, from image block
orientation to DEM extraction and DoD computing. With a view to be able to
apply our method on large image data sets, we didn’t manually check the
results at any intermediary steps. As a consequence we eventually missed gross
GCP errors such as the one mentioned for instance by Verhoeven and Vermeulen
(2016), who iteratively eliminated the GCPs that caused largest errors. In our
case, this uncontrolled use of additional manually marked points resulted in
flawed DoDs for some processing options combinations. The synthesis of our
results and the ones of the literature is that both cumbersome manual marking
in all images and a time-consuming manual check of image orientation results
are required so that manually marked GCPs would improve the photogrammetric
processing.

To our opinion, the fact that the Time-SIFT method allows for a correct
computation of DoDs has at least two explanations. The first one is linked to
the use of tie points that were computed between images of different dates.
Image block geometries of different dates are hence coherent by construction
and consequently, DEMs spatial references are consistent and allow for a
proper estimation of their difference. The second is linked with
autocalibration. We postulate that the Time-SIFT method has also benefits for
the SfM autocalibration step. Autocalibration of archival aerial imagery may
indeed be sensitive, as noted for instance by Aguilar et al. (2013), and can
be even more difficult with SfM algorithms. This is due to the geometry of the
archival images acquisitions and its inadequacy with the requirements of SfM
algorithms. First, overlaps in archival aerial imagery are small (60%)
compared to the overlaps required for SfM algorithms (80%, or even 90%).
Second, in our study, with altitudes higher than 2700 meters and height
difference on ground lesser than 400 m, ground is mostly flat relatively to
flying height. As a consequence, acquisition geometry of archival aerial
imagery constitutes a non-ideal case for autocalibration, with strong possible
correlations between estimated interior and exterior orientations, in
particular flying height and focal length. This correlation may result in the
doming effect observed by James and Robson (2014) on UAV images and that we
also observed in some processing scenarios. Our hypothesis is that
autocalibration in a same image block of the four cameras used at four
different dates, with automatically computed multi-temporal tie points, allows
for a lesser correlation of interior and exterior orientation and hence a
better result.

Qualitative analyses of the DoDs obtained with the Time-SIFT method also
demonstrated its potential. DoDs obtained using a limited number of GCPs
indeed unambiguously detected and quantified past 3-D changes. As orthophotos
greatly changed in texture and gray levels from year to year due to the
significant evolution of land management and land cover on the area (Vinatier
and Arnaiz, 2018), the use of DoDs allowed to detect 3-D changes that would
probably be missed by orthophoto analysis. It is also worth noting that the
magnitudes of the changes mapped by our method are small relatively to the
changes observed with DoDs on volcanoes (e.g. Gomez, 2014) and on glaciers
(e.g. Mertes et al., 2017; Mölg and Bolch, 2017). To the best of our
knowledge, only Bakker and Lane (2017) presented results with 3-D changes
smaller than $\pm$3 meters. To achieve this precision, they used an external
lidar dataset and the a priori knowledge of stable areas for the fine co-
registration of multi-temporal DEMs. Furthermore, the study of anthropogenic
changes from DoDs rather than orthophotos is rarely encountered in the
literature, except in Sevara et al. (2017), who also succeeded in mapping 3-D
changes related to quarries thanks to a fine co-registration of each DEM with
an external lidar DEM prior to photogrammetric DEMs differencing.

With an extensive test of different processing scenarios, we showed that the
Time-SIFT method may be advantageous for a first blind exploration of archival
aerial imagery for detection of 3-D changes. Even if our studies already
covers a time span of three decades and an area of 170 km2, the results may be
different with other time spans and/or on other sites, in particular on sites
that would exhibit even bigger changes than ours. Anyhow, the Time-SIFT method
can conveniently and inexpensively be used at first, with no other information
that image information, in order to detected zones with 3-D changes. This
approach - using the Time-SIFT method without any GCP - was already
successfully tested (Feurer et al., 2017). It can favourably be used with
datasets of thousands of images. Users interested in a more detailed and
accurate description of 3-D changes may then use additional information and/or
a dedicated workflow focused on the zones where 3-D changes would have been
detected with the Time-SIFT method.

Other future works should test the robustness of the Time-SIFT method to
bigger time spans and to different landscapes types. Some improvements may
also be done on the interior orientation step. Indeed, by using more fiducial
marks than the 4 marks of the corners, a better model of camera inner
orientation may be estimated. Finally, there is still a need to find methods
that would allow for the use of archival aerial imagery scanned with desktop
scanners instead of photogrammetric scanner. This would necessitate to use
more information from the scanned analogue image borders for instance, in
order to determine a model of scanner deformation.

##  5 Conclusion

In this paper we proposed and tested a new method, termed Time-SIFT, which
allows for the detection of 3-D changes from archival aerial imagery with
almost exclusively image information. We showed that the Time-SIFT method
allowed for the computing of informative DEMs of difference, in which 3-D
changes could be detected and characterised. Due to the fact that the Time-
SIFT method can be applied automatically and does not require neither
photogrammetric expertise nor additional data, this new method may pave the
way to a more extensive use of the worldwide aerial imagery archive. This
method hence constitutes an additional tool in geosciences for the detection
of past 3-D changes. This work set the stage of two types of new studies.
First this method has now to be tested in different contexts and with other
images datasets in order to determine its robustness. Second the worldwide
archival aerial imagery can be explored with the Time-SIFT method so that past
3-D changes may be discovered or characterised.

The base hypothesis of our work was to lessen as much as possible the use of
external data and to make available to the broader audience the multi-temporal
3-D information of archival aerial imagery. Our work hence focused on easily
available and/or inexpensive data and methods. It is indeed a prime criteria
so that it can further be applied on larger datasets within as much different
contexts as possible and by the widest community.

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##  Appendix A Distribution of error metrics

Figure 8: Boxplots of (a) alignment quality, (b,c) the difference between
computed and reference DEM, respectively the coarse globale and fine local
lidar DEM and (d) DoD quality . P-values issued from Kruskal-Wallis tests
comparing each modality side-by-side are given.