Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests
This addresses the need for precise data fusion in remote sensing to improve object recognition in forests, but it is incremental as it applies existing non-parametric techniques to a specific domain.
The paper tackled the problem of aligning multimodal airborne imagery (LiDAR, hyperspectral, photographic) for forest monitoring by applying non-parametric image registration, demonstrating its success in fusing datasets without prior knowledge like ground control points in a survey of woodlands in southern Spain.
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.