Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images

Abstract

The coregistration of heterogeneous geospatial images is useful in various remote sensing applications. Since the number of available data increases and the resolution improves, it is interesting to have an approach as automated, fast, robust and accurate as possible. In this paper, we present a solution based on optical-flow computation. This algorithm called GeFolki allows the registration of images in a non-parametric and dense way. GeFolki is based on a local method of optical flow derived from the Lucas-Kanade algorithm, with a multi-scale implementation, and a specific filtering including rank filtering, rolling guidance filtering and local contrast inversion. The efficiency of our coregistration chain is shown on radar, LIDAR and optical images on Remningstorp forest in Sweden. An analysis of the relevant parameters is investigated for several scenarios. Finally, we demonstrate the accuracy of our coregistration by proposing specific metrics for LIDAR/radar coregistration, and optics/radar coregistration.

Leak Detection in Water Transmission Systems by Multispectral Remote Sensing With Airplane and UAV

“…As a first processing step, the spectral mosaics have been normalized and the data range limited to the range [0, 1] with the aim of eliminating the "no data" value added by Pix4D. The co-registration of the spectral orthomosaics has been done with Gefolki software developed by ONERA [22] and Pix4D (only the UAV multispectral data). The coregistration has been done taking as master the TIR image as the lowest resolution image in both cases; for this purpose a resampling of the VNIR with a bi-cubic spline interpolation algorithm was performed.…”

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

“…These images had a spatial resolution of 1 and 2.5 m and a spectral resolution of 5.2 and 7.8 nm in the visible-near infrared (VNIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2500 nm) domains, respectively. The SWIR image was resampled to 1 m spatial resolution using a nearest neighborhood filter, in order to preserve the spatial information, and registered according to the VNIR image using the Gefolki algorithm [46]. The resulting spectral radiance image was then converted to spectral reflectance using the empirical line method (ELM) [47,48], because of the lack of knowledge about the composition of the local industrial atmosphere.…”

Terriscope: An Optical Remote Sensing Research Platform Using Aircraft and Uas for the Characterization of Continental Surfaces

“…Georeferencing of hyperspectral data is performed with CALIGEO for AISA Fenix data and with PARGE (Schläpfer, 1998) for HyspexMjolnir data. The registration between the different L1 data (images, or raster derived from point cloud) will be done with GEFOLKY software, developed by ONERA (Plyer, 2015;Brigot, 2016). The interfaces between the different modules and the graphical user interface are still under development.…”