A few-shot high-resolution remote sensing image semantic segmentation method

Semantic segmentation of high-resolution Unmanned Aerial Vehicle (UAV) remote sensing images plays a crucial role in environmental monitoring, urban planning, agricultural assessment, and disaster management. Semantic segmentation methods that are based on deep learning have demonstrated superior performance; however, they rely on large amounts of annotated data, and thus their performance significantly degrades in small-sample scenarios. To obtain better performance on small-scale remote sensing semantic segmentation datasets, methods combining knowledge distillation and semi-supervised learning are proposed. These methods use models pre-trained on large-scale natural image datasets (such as ImageNet) to guide the training of student models on target datasets directly, achieving significant performance gains. However, the feature distribution of natural image datasets differs significantly from that of remote sensing image datasets. Therefore, student models, directly guided by teacher models pre-trained on natural image datasets, often struggle to obtain the optimal performance, especially when few samples are labeled in the target domain. Whether introducing a medium-scale remote sensing dataset as an intermediate domain between natural image datasets and the target remote sensing dataset can further improve model performance is a question worth exploring. This study proposed a few-shot remote sensing image semantic segmentation method that combined multi-stage knowledge distillation (MKD) and semi-supervised learning (SSL) to progressively bridge domain gaps and leverage unlabeled data. The experimental results on the Erhai UAV dataset (EH) show that the proposed MKD + SSL method achieves a mean IoU of 77.05% with only 880 labeled samples, outperforming the widely used single-stage KD method by + 3.06% mIoU, with per-class IoU gains up to +(2.17% − 5.21%). On the Cityscapes benchmark, our framework further surpasses state-of-the-art methods such as UniMatch, achieving a + 1.5% and + 1.4% improvement in mIoU under 1/16 and 1/8 labeled settings, respectively. These results demonstrate that the proposed method effectively enhances segmentation accuracy in few-shot settings and generalizes well across diverse datasets, with wide practical value.