## 1 Introduction
If a CV \(\subset\) AI application in operating mode is implemented on-board a spaceborne platform of an EO imaging sensor to provide imagery with intelligence (semantics), then Future Intelligent EO imaging Satellites (FIEOS), conceived in the early 2000s [144], become realistic, such as future intelligent EO small satellite constellations. In EO small satellite constellations provided with no on-board radiometric _Cal_ subsystem, improved time resolution is counterbalanced by inferior radiometric _Cal_ capabilities, considered mandatory by the GEO-CEOS QA4 EO _Cal_/_Val_ guidelines [3] to guarantee interoperability of multiple platforms and sensors within and across constellations. The visionary goal of AI4 Space is realistic, based on the recent announcement of an RTD project focused on future intelligent small satellite constellations. The quote is: \"an Earth-i led consortium will develop a number of new Earth Observation technologies that will enable processes, such as the enhancement of image resolution, cloud-detection, change detection and video compression, to take place on-board a small satellite rather than on the ground. This will accelerate the delivery of high-quality images, video and information-rich analytics to end-users. On-board cloud detection will make the tasking of satellites more efficient and increase the probability of capturing a usable and useful image or video. To achieve these goals, 'Project OVER9S' will implement, test and demonstrate very high-resolution optical image analysis techniques, involving both new software and dedicated hardware installed on-board small satellites to radically increase their ability to process data in space. The project will also determine the extent to which these capabilities could be routinely deployed on-board British optical imaging satellites in the future\" [145]. Fifth, in the RS common practice, the potential impact of the ESA EO Level 2 product definition is relevant because it makes explicit, once and for all, the undisputable true fact that atmospheric effect correction [12; 56; 68; 69; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121], adjacency and topographic effect corrections [147; 148; 149; 150; 151; 150; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190; 191; 192; 193; 194; 195; 196; 197; 198; 199; 199; 199; 190; 191; 193; 195; 196; 197; 198; 199; 199; 199; 199; 190; 192; 193; 195; 196; 197; 198; 199; 199; 199; 199; 198; 199; 199; 199; 199; 190 cloud and cloud-shadow masks, since atmospheric, topographic and BRDF effect corrections are LC class-dependent. This is tantamount to saying that, to become better posed for automatic numerical solution (requiring no human-machine interaction), an inherently ill-conditioned CV algorithm for EO image correction from atmospheric, topographic, adjacency and BRDF effects needs to be run on a stratified (masked, layered, class-conditional, driven-by-prior-knowledge) basis, i.e., it should run separately on informative EO image strata (masks, layers). A stratified (masked, layered, class-conditional, driven-by-prior-knowledge) approach to CV complies with well-known criteria in the equivocal domain of _information-as-data-interpretation_[18]. * Well-known in statistics, the principle of statistic stratification states that \"stratification will always achieve greater precision provided that the strata have been chosen so that members of the same stratum are as similar as possible in respect of the characteristic of interest\" [152].