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.
This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.
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