Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests

TL;DR

This paper demonstrates that non-parametric image registration techniques can effectively align multimodal airborne remote sensing data of forests, enabling precise data fusion without prior dataset relationship knowledge.

Contribution

It introduces the application of non-parametric registration methods for multimodal airborne imagery, showing their effectiveness in forest monitoring without ground control points.

Findings

Successful fusion of multimodal datasets in woodland surveys

Non-parametric registration effective without prior dataset interrelation knowledge

Enhances object recognition accuracy in airborne imagery

Abstract

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.