Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images

With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image. We propose a simple framework that ease the creation of datasets for the training of deep nets targeting optical image reconstruction, and for the validation of machine learning based or deterministic approaches. These methods are quite different in terms of input images constraints, and comparing them is a problematic task not addressed in the literature. We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images. We generate several datasets to compare the reconstructed images from networks that use a single pair of SAR and optical image, versus networks that use multiple pairs, and a traditional deterministic approach performing interpolation in temporal domain.Comment: 17 page

Automatic reconstruction of urban wastewater and stormwater networks based on uncertain manhole cover locations

International audienceAccurate maps of sewer and stormwater networks in cities are mandatory for an integrated management of water resources. However, in many countries this information is unavailable or inaccurate. A new two-fold mapping method is put forward. The first step consists in using imageprocessing techniques to detect buried network surface elements such as manhole covers on very high resolution aerial imagery. The second step consists in connecting them automatically using a tree-shaped graph constrained by industry rules. The method is tested on Prades-le-Lez, Southern France. The shape and topology of the reconstructed network are compared to the actual ones.The impact of the detected objects’ density is also assessed

Quasimodal expansion of electromagnetic fields in open two-dimensional structures

International audienc

Adaptive perfectly matched layer for wood's anomalies in diffraction gratings

International audienceWe propose an Adaptive Perfectly Matched Layer (APML) to be used in diffraction grating modeling. With a properly tailored co-ordinate stretching depending both on the incident field and on grating parameters, the APML may efficiently absorb diffracted orders near grazing angles (the so-called Wood's anomalies). The new design is implemented in a finite element method (FEM) scheme and applied on a numerical example of a dielectric slit grating. Its performances are compared with classical PML with constant stretching coefficient