Abstract: The invention relates to computer-implemented method for the 3D reconstruction of a ground surface area by stereophotogrammetry, comprising the steps of: determining corrected Rational Polynomial Camera, RPC, models by performing bundle adjustment (BA) of original RPC models each provided with an image of a set of images of the ground surface area acquired by a remote imaging sensor and each associated to a corresponding original projection function ({Pm}) from a 3D object space to a 2D image space, wherein determining the corrected RPC models comprises determining corrected projection functions ({Pmcor}) from the 3D object space to the 2D image space; and determining (PC) a 3D point cloud representative (3DPC) of the ground surface area by triangulation, based on the corrected RPC models, of stereo correspondences within images of the set of images.

Abstract: The invention relates to computer-implemented method for the 3D reconstruction of a ground surface area by stereophotogrammetry, comprising the steps of: determining corrected Rational Polynomial Camera, RPC, models by performing bundle adjustment (BA) of original RPC models each provided with an image of a set of images of the ground surface area acquired by a remote imaging sensor and each associated to a corresponding original projection function ({Pm}) from a 3D object space to a 2D image space, wherein determining the corrected RPC models comprises determining corrected projection functions ({Pmcor}) from the 3D object space to the 2D image space; and determining (PC) a 3D point cloud representative (3DPC) of the ground surface area by triangulation, based on the corrected RPC models, of stereo correspondences within images of the set of images.

Abstract: The invention relates to a method for processing a sequence of top view image frames ({ItLR}t=0T) of low resolution of a same terrestrial location, each top view image frame ({ItLR}t=0T) having pixels and pixel values, comprising the following steps: choosing (S0) one top view image frame (I0LR), called reference frame (I0LR), among the top view image frames ({ItLR}t=0T), estimating (S1) motion fields (Ft?0, ME) between each top view image frame ({ItLR}t=1T) and the reference frame (I0LR) by using a first neural network (NN1), encoding (S1?) the top view image frames ({ItLR}t=1T) to produce convolutional features ({JtLR}t=1T) extracted respectively from the top view image frames ({ItLR}t=1T) by using a second neural network (NN2), aggregating (S2, S3, Shift-and-add, SPMC) pixels of the convolutional features ({JtLR}t=1T) to positions ({JtHR}t=1T) in a high resolution grid (HRG) using the motion fields (Ft?0, ME) to obtain aggregated features (JHR), decoding (S4) by a decoder network (DN) the aggregated fea