## 1 Introduction
_Vision_ is a cognitive (_information-as-data-interpretation_) problem [18] very difficult to solve because: (i) non-deterministic polynomial (NP)-hard in computational complexity [87; 116], (ii) inherently ill-posed in the Hadamard sense [23; 75; 117], because affected by: (I) a 4D-to-2D data dimensionality reduction from the 4D geospatial-temporal scene-domain to the (2D, planar) image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap from ever-varying sub-symbolic sensory data (sensations) in the physical world to stable symbolic percepts in the mental model of the physical world (modeled world, world ontology, real-world model) [13; 18; 19; 20; 21; 22; 23; 24]. Since it is inherently ill-posed, vision requires _a priori_ knowledge in addition to sensory data to become better posed for numerical solution [32; 33]. If the aforementioned working hypothesis holds true, then the complexity of SCBIR + SEIKD is not inferior to the complexity of _vision_, acknowledged to be inherently ill-posed and NP-hard. To make the inherently-ill-posed CV problem better conditioned for numerical solution, a CV system is required to comply with human visual perception. In other words, a CV system is constrained to include a computational model of human vision [13; 76; 87; 88; 89; 90; 91; 92; 93; 94], i.e., 'Human vision \(\rightarrow\) CV'. Hence, dependence relationship: 'Human vision \(\rightarrow\) CV \(\supset\) EO-IU in operating mode \(\supset\) NASA EO Level 2 product \(\rightarrow\) ESA EO Level 2 product \(\rightarrow\) [EO-SCBIR + SEIKD = AI4 DIAS] \(\rightarrow\) GEO-GEOSS' becomes our working hypothesis (to be duplicated in the body text). Equivalent to a first principle (axiom, postulate), this equation can be considered the first original contribution, conceptual in nature, of this research and technological development (RTD) study. An improved (\"augmented\") and more restrictive ESA EO Level 2 product definition could be defined to account for bidirectional reflectance distribution function (BRDF) effect correction, in addition to atmospheric, topographic and adjacency effects correction, to model surface anisotropy in multi-temporal multi-angular EO image data cubes [2; 68; 69; 74; 118; 119; 120; 121]. A surface that reflects the incident energy equally in all directions is said to be Lambertian, where reflectance is invariant with respect to illumination and viewing conditions, see Figure 3. On the contrary, a surface is said to be anisotropic when its reflectance varies with respect to illumination and/or viewing geometries. These changes are driven by the optical and structural properties of the surface material. In other words, in EO image pre-processing (enhancement) for radiometric _Cal_, BRDF effect correction is LC class-specific [68; 69; 74; 118; 119; 120; 121]. The LC class-specific task of BRDF correction is to derive, for non-Lambertian surfaces, spectral albedo (bi-hemispherical reflectance, BHR) values, defined over all directions [2; 74; 118; 120; 121], from either SURF or TOARF values where the Lambertian surface assumption holds [71; 72; 73; 74; 118]. Our working hypothesis, depicted in Figure 4, when translated into symbols of the standard UML for graphical modeling of object-oriented software [86] can be formulated as follows (refer to the caption of Figure 4):  
\[ \begin{array}{l}\text{'Human vision $\rightarrow$ CV $\supset$ EO-IU in operating mode $\supset$ NASA EO Level 2 product}\\ \rightarrow\text{ESA EO Level 2 product $\rightarrow$ [EO-SCBIR + SEIKD = AI4 DIAS] $\rightarrow$ GEO-GEOSS'.}\end{array}  \tag{1}\]  
Duplicated from the caption of Figure 4 and regarded as the first original contribution of this research and technological development (RTD) study, Equation (1) shows our working hypothesis as dependence relationship, equivalent to a first principle (axiom, postulate).