Category Archives: Projets2019

Description En imagerie radar à synthèse d’ouverture certaines structures urbaines peuvent produire des réponses ponctuelles beaucoup plus fortes que leur entourage. Cette réponse très forte couplée à la réponse impulsionnelle (sinc) de l’instrument produit une tache qui dépasse largement le … Continue reading →

This project is only for hackers, proficient with unix and python. The 2019 IEEE GRSS Data Fusion Contest track 3 [2] concerns multi-view stereo: Given multi-view images for each geographic tile, the objective is to predict semantic labels and a … Continue reading →

A naive way to create a “fake” orthophoto from a single satellite image consists in projecting the photo into a horizontal surface.  This is correct when the ground is flat, and the ground is often nearly flat, however it does … Continue reading →

The famous and elegant Patch Match algorithm [1], seen on the course, finds a dense set of correspondences between two images, by iteratively updating an initial random disparity chart.  These disparities are then “triangulated” to obtain a cloud of points … Continue reading →

The projection and localization functions of satellite images are often [1,2] represented by rational functions of degree 3 in 3 variables.  This gives a very precise approximation of the physical model. The advantage of this representation is that it provides … Continue reading →

The objective of this project is to evaluate the state-of-the-art on stereo matching algorithms for satellite images. The starting point will be the analysis of a recent CNN-based stereo matching algorithm proposed in [1], or a more recent article judged … Continue reading →

The optical stereo pipeline described in the course is a simplified version of the S2P pipeline [1], which was developed for high resolution pushbroom satellites such as Pleiades or WorldView. One of the particularities of these satellites is that the … Continue reading →

The epipolar plane image analysis method was introduced by Bolles [1] and later refined by Kim et.al [2], as a way to estimate depth from a sequence of images taken from different points lying along a straight line. The method was … Continue reading →

Even though noise present in satellite optical images is a mixture of Poisson and Gaussian noise, the great majority of denoisers work only in the case of Gaussian noise. A common way to circumvent this inadequacy consists in Approximating the … Continue reading →

Context The process of acquiring interferometric hyperspectral images from airborne or satellite sensors [1,2] comprises several steps, including : Rectification of the image sequence to a common epipolar geometry to build an epipolar plane image (EPI) sequence. Decomposition of each … Continue reading →