Fig. 3.

Network setup for the four domain problem. (a) Reflectance data r is introduced into a primary autoencoder (i.e. $r\to z\to \widehat{r}$), generating a low-dimensional translation of spectral and spatial data. (b) A secondary autoencoder $z\to z^{\prime }\to \widehat{z}$ transforms this first domain into a two-dimensional domain z′, where the dataset can be represented. The same codeword z can be used for classification (c). Conditional sample generation is achieved with a set of small multilayer perceptron Least-Squares GANs (d), with multiple decoders to avoid mode collapse (e). Optical properties estimation is achieved via an MLP non-linear regressor, which is trained with domain randomization, using spectra generated by giving random OP values to a deterministic semi-empirical function (f). The following paths represent each of the objectives in Fig. 1, as follows: $\overline{AB}$ (Feature Extraction), $\overline{\mathit{\text{ABEF}}}$ (Visualization), $\overline{\mathit{\text{ABG}}}$ (Classification), $\overline{H_0/\dots /H_{n}CD}$ (Generation), $\overline{A{A}^{\prime }J}$ (pixel-wise OP estimation). Black arrows are real connections in the graph, while orange connections represent copying operations.