Mapping Surface-Cover Components in Desert Steppe from UAV RGB Imagery Using Interpretable Features and Multiscale Superpixel Probability Fusion

Unmanned aerial vehicle (UAV) red–green–blue (RGB) imagery provides low-cost, high-resolution observations of desert-steppe cover, but overlapping colors and spatial heterogeneity complicate classification. We evaluated random forest (RF) with multiscale superpixel probability fusion (RF-MSPF) using 19 patches from nine images and 335,675 reference pixels representing herbaceous vegetation, woody or semi-woody vegetation, visible dead plant material, and bare substrate. Five interpretable RGB features supplied RF class probabilities, aggregated within Simple Linear Iterative Clustering (SLIC) regions at three scales. Source-image-grouped five-fold validation increased mean overall accuracy (OA) from 0.8020 ± 0.0573 for RF to 0.8691 ± 0.0367 for RF-MSPF (mean ± sample standard deviation), with gains in every fold. Equal-weight fusion achieved 0.8704 ± 0.0367. On the fixed development split, RF-MSPF achieved OA = 0.8806, macro-averaged F1 (Macro-F1) = 0.8742, and mean intersection over union (mIoU) = 0.7840. Ablations identified regional aggregation as the main improvement, with further gains from multiscale fusion; consistency weighting added only 0.024 percentage points on that split and showed no consistent advantage. This transparent, reproducible workflow improved classification within this dataset.