Future CNES high resolution instruments for remote sensing missions will lead to higher data-rates because of the
increase in resolution and dynamic range. For example, the ground resolution improvement has induced a data-rate
multiplied by 8 from SPOT4 to SPOT5  and by 28 to PLEIADES-HR .
Innovative "smart" compression techniques will be then required, performing different types of compression inside a
scene, in order to reach higher global compression ratios while complying with image quality requirements. This socalled
"selective compression", allows important compression gains by detecting and then differently compressing the
regions-of-interest (ROI) and non-interest in the image (e.g. higher compression ratios are assigned to the non-interesting
Given that most of CNES high resolution images are cloudy , significant mass-memory and transmission gain could
be reached by just detecting and suppressing (or compressing significantly) the areas covered by clouds.
Since 2007, CNES works on a cloud detection module  as a simplification for on-board implementation of an already
existing module used on-ground for PLEIADES-HR album images . The different steps of this Support Vector
Machine classifier have already been analyzed, for simplification and optimization, during this on-board implementation
study: reflectance computation, characteristics vector computation (based on multispectral criteria) and computation of
the SVM output.
In order to speed up the hardware design phase, a new approach based on HLS  tools is being tested for the VHDL
description stage. The aim is to obtain a bit-true VDHL design directly from a high level description language as C or