  Applications based on synergistic integration of optical imagery and LiDAR
data are receiving a growing interest from the remote sensing community.
However, a misaligned integration between these datasets may fail to fully
profit the potential of both sensors. In this regard, an optimum fusion of
optical imagery and LiDAR data requires an accurate registration. This is a
complex problem since a versatile solution is still missing, especially when
considering the context where data are collected at different times, from
different platforms, under different acquisition configurations. This paper
presents a coarse-to-fine registration method of aerial/satellite optical
imagery with airborne LiDAR data acquired in such context. Firstly, a coarse
registration involves extracting and matching of buildings from LiDAR data and
optical imagery. Then, a Mutual Information-based fine registration is carried
out. It involves a super-resolution approach applied to LiDAR data, and a local
approach of transformation model estimation. The proposed method succeeds at
overcoming the challenges associated with the aforementioned difficult context.
Considering the experimented airborne LiDAR (2011) and orthorectified aerial
imagery (2016) datasets, their spatial shift is reduced by 48.15% after the
proposed coarse registration. Moreover, the incompatibility of size and spatial
resolution is addressed by the mentioned super-resolution. Finally, a high
accuracy of dataset alignment is also achieved, highlighted by a 40-cm error
based on a check-point assessment and a 64-cm error based on a check-pair-line
assessment. These promising results enable further research for a complete
versatile fusion methodology between airborne LiDAR and optical imagery data in
this challenging context.
