  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.
