Feature/add random forest regression

See merge request !5

Added random_state to KMeansRSImage.get_random_spectra_from_each_cluster(). Added kwargs to classify_image() to allow passing init args to classifiers. Fixed CFG.spechomo_n_clusters, CFG.spechomo_classif_alg and CFG.spechomo_kNN_n_neighbors ignored in spectral homogenization. Added test_predict_by_machine_learner__RFR_L8_S2().

Fixed issue #95 (DEM_Creator: passing only 2 UTM coordinates causes No-Data triangles at left and right side of DEM).

Revised nodata value handling of RSImage_ClusterPredictor.predict() and added possibility to statically set the nodata value of the predicted image.

Revised image classification algorithms to speed them up in multiprocessing. Added multiprocessing tests for classification algorithms.

Restricted tree depth of Random Forest Regressors to 10 to avoid overfitting and to drastically reduce file size of classifiers.

No-data value of input/output image of spectral homogenization and no-data value of intermediate classification maps are now separately handled to solve nodata issue during spectral homogenization.

Added band names and center wavelengths to products of ReferenceCube_Generator.resample_image_spectrally().