ULKNet:Rethinking Large Kernel CNN with UNet-Attention for Remote Sensing Images Semantic Segmentation

Since the advent of the Transformer, the Vision Transformer (ViT) model has become the optimal solution for extracting semantic information in remote sensing image semantic segmentation tasks. However, recent research has discovered that using larger convolutional kernels to extract global information can achieve performance comparable to, or even better than the ViT models. This finding inspires us to redesign the convolutional neural network structure and innovatively construct a novel parallel global-local module based on large kernel convolution, in order to better extract semantic information and local contextual information. Following this design approach, we propose ULKNet, a pure CNN architecture that achieves performance comparable to the ViT model. Through experiments on the ISPRS Vaihingen and Potsdam datasets, We respectively achieve mIoU 82.7% and 86.0% mIoU scores. This finding demonstrates that a well-designed large kernel CNN network structure can provide a larger and more effective receptive field, thereby better extracting semantic information.