Enhanced remote sensing image feature classification using STFF-PSPNet

Semantic segmentation of remotely sensed images is crucial for urban planning and change detection, yet faces issues like sample imbalance and low data quality. This study compiles a GF-2 image dataset and refines the PSPNet model. Weights of different class samples were adjusted to prioritize minority classes, mitigating sample imbalance's impact on classification. Data augmentation enhanced dataset quality. By replacing ResNet with the STFF network for better global feature extraction, adding attention modules, and using a combined loss function, the improved model shows excellent performance. It achieves a mAcc of 90.32 %, mIoU of 76.04 %, and a Dice coefficient of 85.15 %. Comparison with other models verifies its superiority, and tests on public datasets prove strong generalization, offering valuable insights for remote sensing image processing.