|Title||Efficient FFT-accelerated approach to invariant optical-LIDAR registration|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Wong, A., and J. Orchard|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Pagination||3917 - 3925|
|Keywords||Fast Fourier Transform, fast Fourier transforms, feature mapping transformation optimization, FFT-accelerated approach, geometric distortions, geometric transformation estimation process, geophysical techniques, image registration, LIDAR images, light detection and ranging image registration, multimodal invariant registration, optical image registration, optical radar|
This paper presents a fast Fourier transform (FFT)-accelerated approach designed to handle many of the difficulties associated with the registration of optical and light detection and ranging (LIDAR) images. The proposed algorithm utilizes an exhaustive region correspondence search technique to determine the correspondence between regions of interest from the optical image with the LIDAR image over all translations for various rotations. The computational cost associated with exhaustive search is greatly reduced by exploiting the FFT. The substantial differences in intensity mappings between optical and LIDAR images are addressed through local feature mapping transformation optimization. Geometric distortions in the underlying images are dealt with through a geometric transformation estimation process that handles various transformations such as translation, rotation, scaling, shear, and perspective transformations. To account for mismatches caused by factors such as severe contrast differences, the proposed algorithm attempts to prune such outliers using the random sample consensus technique to improve registration accuracy. The proposed algorithm has been tested using various optical and LIDAR images and evaluated based on its registration accuracy. The results indicate that the proposed algorithm is suitable for the multimodal invariant registration of optical and LIDAR images.