## 1 Introduction
This hybrid workflow alternates deductive/prior knowledge-based and inductive/learning-from-data inference units, starting from initial conditions provided by a first-stage prior knowledge-based decision tree for static (non-adaptive to data) color naming, such as the Spectral Classification of surface reflectance signatures (SPECL) decision tree [73] implemented within the ATCOR commercial software toolbox. Categorical variables generated as output by the two processing blocks identified as “pre-classification” and “classification” are employed as input by the subsequent processing blocks to stratify (mask) unconditional numeric variable distributions, in line with the statistic stratification principle [152]. Through statistic stratification, inherently ill-posed inductive learning-from-data algorithms are provided with _a priori_ knowledge required in addition to data to become better posed for numerical solution, in agreement with the machine learning-from-data literature [32; 33]. In the Atmospheric/Topographic Correction for Satellite Imagery (ATCOR) commercial software product, several per-pixel (spatial context-insensitive) deductive spectral rule-based decision trees are implemented for use in different stages of an EO image enhancement pipeline [71; 72; 73; 74; 161; 162], see Figure 6.4 According to [PERSON] and [PERSON] [71; 72], \"pre-classification as part of the atmospheric correction has a long history, e.g., in the NASA's processing chain for MODIS\" [57], also refer to [12; 56]. One of the ATCOR's prior knowledge-based per-pixel decision trees delivers as output a haze/cloud/water (and snow) classification mask file (\"image_hcw.bsq\"), see Figure 6.5 In addition, ATCOR includes a so-called prior knowledge-based decision tree for Spectral Classification of surface reflectance signatures (SPECL) [73], see Figure 6.6 Unfortunately, SPECL has never been tested by its authors in the RS literature, although it has been validated by independent means [161; 162]. Commissioned by ESA, the Sentinel-2 (atmospheric and topographic) Correction Prototype Processor (Sen2 Cor) is not run systematically at the ESA ground segment. Rather, it can be downloaded free-of-cost from an ESA website to be run on the user side [11; 12; 44]. Hence, the ESA Sen2 Cor software toolbox does not satisfy the ESA EO Level 2 product requirements specification, refer to Section 1. The existing sensor-specific Sen2 Cor prototype processor, sketched in Figure 11, adopts the same feedforward workflow of the popular ATCOR commercial software product [71; 72; 73]. The Sen2 Cor software toolbox accomplishes, first, one SCM product generation from TOARF values by means of a per-pixel (spatial context-insensitive) prior spectral knowledge-based (static, non-adaptive to data) decision tree, whose SCM legend is shown in Figure 6.7 Next, a stratified (class-conditional, driven-by-knowledge) MS image radiometric correction approach is adopted to transform TOARF into SURF values, where SURF values are sequentially corrected for atmospheric, adjacency and topographic effects stratified by the same SCM product generated at first stage from TOARF values. \begin{table}
\begin{tabular}{c l l} \hline \hline \multirow{2}{*}{**Label**} & \multicolumn{2}{l}{**ATCOR-2/3/4 Spectral pre-Classification,**} & \multirow{2}{*}{**Order of Detection**} \\  & **Land Cover (LC) Class Definition** & \\ \hline
0 & Background & \\
1 & Cloud shadow & 5 \\
2 & Cirrus—Thin over water & 10 \\
3 & Cirrus—Medium over water & 11 \\
4 & Cirrus—Thick over water & 12 \\
5 & Land (if not 0 to 5 or 6 to 17) & 17 \\
6 & Saturated (if (DN > 0.9 * DNmax), then saturated) & 1 \\
7 & Snow/ice & 6 \\
8 & Cirrus—Thin over land & 7 \\
9 & Cirrus—Medium over land & 8 \\
10 & Cirrus—Thick over land & 9 \\
11 & Haze—Thin/medium over land & 13 \\
12 & Haze—Thick/medium over land & 14 \\
13 & Haze—Thin/medium over water & 15 \\
14 & Haze—Thick/medium over water & 16 \\
15 & Cloud over land & 3 \\
16 & Cloud over water & 4 \\ \hline \hline \end{tabular}
\end{table}