The remote sensing image segmentation of land cover based on multi-scale attention features

Segmentation of land cover in remote sensing images is a task that involves interpreting remote sensing data using machine vision. Satisfying segmentation results in agriculture and forestry regions can guide land resource management, natural environment protection, urban construction, and the distribution of agricultural products. However, the performance of the widely used deep learning segmentation model on high-resolution remote sensing segmentation datasets in agriculture and forestry regions needs to be improved. To solve the problems of poor accuracy and loss of context information in remote sensing image semantic segmentation, this paper proposes an improved semantic segmentation network architecture. The model utilizes multi-scale feature extraction, deploys a multi-layer attention feature fusion module and an up-sampling fusion module to capture high-quality multi-scale context information, correctly handle scale changes, and help narrow the semantic gap between different levels. Finally, the proposed MLP decoder refers to the dynamic up-sampling operator to aggregate the information at different levels to achieve pixel segmentation. To verify the effectiveness of our proposed model, the researchers conducted experiments on two land cover segmentation datasets. The training process specifically designs data augmentation strategies for remote sensing segmentation tasks to enhance the model’s generalization ability. The final model achieved an mIoU (mean Intersection over Union) of 65.05% on the self-built rural land cover datasets, surpassing the benchmark network UPerNet by 5.92%. On the LoveDA dataset, our model achieved state-of-the-art performance with an mIoU of 53.39%, demonstrating its versatility.