Building Detection Under Forest Canopy Using Physically Interpretable SAR Features and Hybrid Machine Learning Across Multiple Forest Biomes

Forests cover nearly one-third of the Earth’s land surface and are subject to increasing anthropogenic pressure, including unauthorised construction, infrastructure expansion, and habitat fragmentation. Detecting buildings concealed beneath forest canopy is essential for environmental monitoring and territorial surveillance, yet optical satellite imagery fails under persistent cloud cover and dense vegetation, and no existing approach provides reliable detection across contrasting forest ecosystems. We address this gap by proposing a hybrid SAR-based detection framework that combines twelve physically interpretable Scattering-Informed SAR Features (SISF)—derived from electromagnetic scattering theory across amplitude, polarimetric, temporal, and texture dimensions—with a universal Convolutional Neural Network, integrated through probability-level fusion with isotonic regional calibration. Rather than relying on individual feature thresholds, the framework identifies buildings through their characteristic multidimensional scattering signature—a combination that remains discriminative across biomes where any single SAR descriptor would fail. The framework was evaluated on a novel 7721-object multi-biome benchmark spanning forest-steppe (Kazakhstan), boreal forest (Komi Republic, Russia), and tropical rainforest (Brazil, Pará). The final Fusion + Regional Calibration model achieved an overall F1-score of 0.803 on the held-out test set (N = 1545), outperforming single-model baselines by up to 17 percentage points. Regional F1-scores ranged from 0.727 (Kazakhstan) to 0.944 (Komi Republic), with detection performance remaining robust under partial canopy occlusion (F1 = 0.911, versus 0.903 for unobscured buildings). An empirical inverse relationship between hard negative proportion and detection F1-score was identified within each biome, with the 28–35% proportions present in our sampled regions reported as a preliminary observation rather than a generally optimal range—a dataset design finding not previously reported in the literature. The proposed framework provides a physically interpretable solution for SAR-based building detection under forest canopy, demonstrating consistent performance across three contrasting forest biomes, with direct applications to environmental monitoring and territorial surveillance in forested regions.