Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveiraet al.(2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

The deployment of an application onto a multicore archi- tecture is often a long and difficult process. This is due to the fact that the characteristics of both the architecture and the application are taken into account late in the development process. It's therefore necessary to have tools pruning the solution space efficiently and accurately. In order to define such a tool, this work defines the basic metrics and step for a design space exploration ("DSE") flow. To this end our proposal have been tested and validated for a space application that requires both a high computing power and an architecture compatible with the space constraints. The results obtained are promising and have shown the viability of the approach.

The input pupil of interferometry-based observation instruments is necessarily segmented. Since the Optical Transfer Function (OTF) of any optical instrument observing in incoherent light is the auto-correlation of its input pupil, it follows that any combination of size and position of each pupil segment will have an impact on the OTF behavior and therefore on the quality of the output image. The goal of this study is to propose computational geometry methods allowing to find pupil geometries leading to an isotropic OTF support with a controlled redundancy of viewed spatial frequencies in the Fourier domain.

Texture analysis has been widely investigated in the monospectral and multispectral imagery domains. At the same time, new image sensors with a large number of bands (more than ten) have been designed. They are able to provide images with both fine spectral and spatial sampling, and are called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, using a Gauss-Markov random field (MRF). The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas. These areas are not well characterized by radiometry alone, and so we use the MRF parameters as new features in a maximum-likelihood classification algorithm. The results obtained on Airborne Visible/Infrared Imaging Spectrometer hyperspectral images demonstrate that a better classification is achieved when texture information is included in the analysis.

This paper deals with low bit rate transform coding of images. We point out why the hypotheses used for the characterization of transform coders at high bit rate does not hold at low bit rate, and derive a new analytical formula for the distortion rate function. This formula is tested with JPEG, wavelet and embedded coders.

The scale-space representations and wavelet transform theory have provided us with good singularity detection frameworks. Thanks to such methods, image understanding processes have become more and more powerful. Some computer vision tasks also require a knowledge on the nature of detected singularities. We propose, in this paper, to take into account these requirements, and we present a singularity detection method based on fractional calculus arguments.>

Multiscale Analysis of surfaces allows a hierarchical representation of their composing features. To represent a surface at a given scale, structures that are insignificant at that scale have to be eliminated. A typical example for this approach is cartography. However, the aims of cartographers reach beyond simply gradually eliminating the structures; in the majority of cases, the nature of geomorphological structures which compose the surface have to be preserved across all scales. Thus a global smoothing of the surface is not suitable to solve the present problem, since that would cause inevitably morphological modifications of certain important structures. In fact, the points to be preserved across scale variations are to be chosen interactively by the user. The authors present a surface model which allows them to perform a Multiscale Analysis which takes the importance of local structures into consideration, i.e. structures which are inherent to the relief morphology. From that discrete model we extract a context-dependent Multiscale Analysis Operator which can be isotropic or anisotropic and can be expressed in different forms.>