Reliability-Aware Dual-Stream Self-Training for Semi-Supervised Semantic Segmentation of High-Resolution Remote Sensing Imagery

Abstract

Semantic segmentation of high-resolution remote sensing imagery remains constrained by the high cost of pixel-level annotation. Semi-supervised learning can reduce this dependence by exploiting unlabeled images, but improving pseudo-label reliability often comes at the cost of reduced data coverage. Selecting only stable samples can suppress noise but may exclude complex scenes and minority classes, whereas retaining the complete unlabeled set preserves data diversity but introduces less reliable and class-biased supervision. To address this trade-off, we propose RAPST, a reliability-aware dual-stream self-training framework that combines reliable offline supervision with full-data online learning. In the offline stream, the Class-Aware Image Stability Gate (ISG) selects prediction-stable images while preserving class coverage. In the online stream, EMA-Smoothed Class-Adaptive Pseudo-Label Thresholding (CPT) adapts pixel-selection thresholds according to class-wise pseudo-label statistics. Reliability-Aware Prototype-Guided Category Contrast (PCC) further integrates reliable feature–label pairs from both streams to improve feature discrimination. Experiments on the ISPRS Vaihingen, ISPRS Potsdam, and WHDLD datasets under four annotation ratios show that RAPST achieves the best performance in most dataset–annotation settings, with mIoU improvements of 0.33–6.08 percentage points over the supervised-only baseline. Ablation and mechanism analyses further show that prediction stability is associated with pseudo-label quality, CPT increases supervision coverage for difficult classes with limited precision loss, and PCC improves feature-space separability.