## 3 Related Works in Cloud and Cloud-Shadow Quality Layers Detection
For comparison purposes, three popular computer programs available either open source or free-of-cost for cloud/cloud-shadow detection in spaceborne MS imagery are critically reviewed hereafter, see Table 5.  
* The single-date multi-sensor FMask open source algorithm [58], originally developed for single-date 30 m resolution 7-band (from visible blue, B, to thermal InfraRed, TIR) Landsat-5/7/8 MS imagery, which includes a thermal band as key input data requirement. FMask was recently extended to 10 m/20 m resolution Sentinel-2 MS imagery [59], featuring no thermal band, and to Landsat image time-series (multiTemporal Mask, TMask) [60]. The potential relevance of FMask is augmented by considering that CFmask, an Fmask program version transcoded into the C programming language for increased efficiency, is adopted for cloud, cloud-shadow and snow/ice classification by the LEDAPS and LaSRC algorithms for atmospheric correction in the U.S. Landsat ARD product [139; 140; 141; 142; 143]. Unfortunately, CFMask is affected by known artifacts [63]. Moreover, in a recent comparison of cloud and cloud-shadow detectors, those implemented in LEDAPS scored low among alternative solutions [62]. * The single-date single-sensor ESA Sen2 Cor prototype processor, capable of automated atmospheric, adjacency and topographic effect correction and SCM product generation, including cloud and cloud-shadow detection (refer to the SCM legend shown in Table 1), whose input is an ESA EO Level 1 image, radiometrically calibrated into TOARF values, originally acquired at Level 0 (in dimensionless DNs provided with no radiometric unit of measure) by the ESA Sentinel-2 Multi-Spectral Instrument (MSI) exclusively. The ESA Sen2 Cor prototype processor is distributed free-of-cost by ESA to be run on the user side [11; 12]. Hence, it does not satisfy the ESA EO Level 2 product requirements specification proposed in Section 1. ESA Sen2 Cor incorporates capabilities of the ATCOR commercial software toolbox [71; 72; 73], see Figure 11. ESA Sen2 Cor is affected by known artifacts [44; 47; 61], which may be inherited at least in part from ATCOR. * The multi-date multi-sensor MAJA, developed and run by CNES/CESBIO/DLR [46; 48]. Starting from its name, MAJA incorporates capabilities of the ATCOR commercial software toolbox [71; 72; 73]. MAJA is affected by known artifacts [47; 61], which may be inherited at least in part from ATCOR. Table 5 compares the Sen2 cor, MAJA and FMask computer programs at three-of-five levels of understanding of an information processing system proposed by [PERSON], specifically, information/knowledge representation, system design (architecture) and algorithm (refer to Section 1) [13; 19; 76; 82; 83]. For the sake of brevity, Table 5 omits comparisons of alternative algorithms at the two levels of understanding known as outcome/process requirements specification and implementation (refer to Section 1). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{cThe first observation stemming from Table 5 is that Sen2 cor and MAJA employ deductive (top-down, prior knowledge-based) inference, exclusively, to map MS imagery onto LC classes, in addition to mapping quality layers cloud and cloud-shadow. This is in contrast with biological cognitive systems [108], where hybrid inference combines deductive and inductive inference to take advantage of each and overcome their shortcomings [2; 13; 40; 82; 83], refer to Section 2. The second observation driven from Table 5 is that the Sen2 cor, MAJA and FMask computer programs employ 1D image analysis algorithms exclusively, either pixel-based, i.e., spatial context-insensitive and spatial topology non-preserving, or spatial context-sensitive (e.g., image object-based or local window-based), but spatial topology non-preserving (non-retinotopic) [13; 87; 88; 96; 170]. In CV programs, 1D image analysis is a methodological (structural) drawback because perceptual evidence proves that, in vision, primary spatial topological information (e.g., adjacency, inclusion, etc.)