  There is much current interest in using multi-sensor airborne remote sensing
to monitor the structure and biodiversity of forests. This paper addresses the
application of non-parametric image registration techniques to precisely align
images obtained from multimodal imaging, which is critical for the successful
identification of individual trees using object recognition approaches.
Non-parametric image registration, in particular the technique of optimizing
one objective function containing data fidelity and regularization terms,
provides flexible algorithms for image registration. Using a survey of
woodlands in southern Spain as an example, we show that non-parametric image
registration can be successful at fusing datasets when there is little prior
knowledge about how the datasets are interrelated (i.e. in the absence of
ground control points). The validity of non-parametric registration methods in
airborne remote sensing is demonstrated by a series of experiments. Precise
data fusion is a prerequisite to accurate recognition of objects within
airborne imagery, so non-parametric image registration could make a valuable
contribution to the analysis pipeline.
