The image sensors embedded in the instruments on board of different satellites are a key aspect in the success of a mission of any type such as Earth observation, astronomy or planetary and universe exploration. The quality of the sensor itself as well as the electronics embedded in the instrument suffer from aging, not only due to the usage, but also due to the space environment factors. The outer space harsh environment is composed of charged particles with an incredibly high energy that can get through the shielding of the spacecraft thus causing damage to the sensors, or any part of the spacecraft. After a satellite is placed in orbit, there is no other way to check on its condition than to analyse the data transmitted to the ground stations.   It is well known that we can measure the evolution of the instrument’s detection unit by measuring the variation of the quality of the images taken in orbit. As it has been done in past work, the atypical behaviour can be detected at a pixel level by analysing the images taken in orbit, preferably in complete darkness to discard any parasitic light. Past analysis of the dark images from the sensors on board the Picard, SPOT4 and Sentinel2A mission, proposed a classification of the different atypical behaviour that a pixel can show, such as hot, transitory-hot or RTS. By the end of the mission this defects may even be present in several pixels.   The purpose of this work is to process the dark images obtained on orbit, at the end of the mission PLEIADES, to better understand the behaviour both Panchromatic and Multispectral CCD sensors and the differences in performance with respect to the tests made previously on ground. For this, a description of the framework of the PLEIADES mission and the technical design aspects of the CCD sensors on board are presented. Subsequently, the different image processing techniques used and developed are described, along with the tests made to get to the positive conclusion of no major anomalies detected in the PLEIADES images sensors. The analysis is done by means of new set of functions adjusted to the structure of the data, as well as the same detection software used in the previous analysis. Finally, a discussion is presented along with a further perspective for the analysis of future sets of data.