Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes.

ABSTRACT:Since SPOT1, the French national space centre (CNES) has worked on improving the geometry of Earth observation spacecrafts. The accuracy of sensor calibration is one of the main key points for any Earth observation application such as orthorectification, DEM generation or surface change detection. For the last twenty years CNES has developed two families of methods: absolute methods and relative methods. These methods are used to characterize a pushbroom acquisition along the detector line and the time line. By this way, the viewing directions are measured and the residual of the spacecraft's attitude angles (not restituted by the Attitude and Orbit Control System) is estimated. This information can complete the geometric model of all the scenes acquired by the instrument and is used in all geometric applications. This paper presents new attitude assessment methods taking advantage of the capabilities of Pléiades-HR in terms of agility and focal plane arrangement -panchromatic band and multispectral (MS) bands.

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In order to increase SPOT5 panchromatic resolution, CNES adopted in 1995 a quincunx sampling mode named THR, which is a french acronym for "very high resolution". Such a sampling is produced by two CCD linear arrays shifted in the focal plane and has been shown to meet Shannon requirement at first order. That means a good fit between the sampling and the instrument Modulation Transfer Function (MTF) and thus an optimization of the whole acquisition system. SPOT5 THR is a 2.5 m image obtained through a rather complex processing of the two 5 meter shifted images delivered by the double CCD linear array, consisting in quincunx interpolation, deconvolution and denoising. Quincunx interpolation computes radiometric information over a 2.5m grid while only half the information may be retrieved from the two 5m images, deconvolution compensates for low MTF values for high spatial frequencies and denoising reduces the noise level enhancement due to deconvolution. Apart from the high resolution panchromatic mode, SPOT5 also delivers classical 10m multispectral images covering the green, red and near infrared spectral domains that are almost simultaneously acquired. Merging the 2.5m THR with the 10m multispectral yields a 2.5m multispectral product which proved to be of utmost interest for thematic applications Gathering the main results from the SPOT5 in orbit commissioning period, this paper gives an overview of THR and fusion processing, points out onboard and onground sensitive parameters and finally presents the THR and THX performances

ABSTRACT:The first Pleiades-HR satellite, part of a constellation of two, has been launched on December 17, 2011. This satellite produces high resolution optical images. In order to achieve good image quality, Pleiades-HR should first undergo an important 6 month commissioning phase period. This phase consists in calibrating and assessing the radiometric and geometric image quality to offer the best images to end users. This new satellite has benefited from technology improvements in various fields which make it stand out from other Earth observation satellites. In particular, its best-in-class agility performance enables new calibration and assessment techniques. This paper is dedicated to presenting these innovative techniques that have been tested for the first time for the Pleiades-HR radiometric commissioning. Radiometric activities concern compression, absolute calibration, detector normalization, and refocusing operations, MTF (Modulation Transfer Function) assessment, signal-to-noise ratio (SNR) estimation, and tuning of the ground processing parameters. The radiometric performances of each activity are summarized in this paper.