Semantic segmentation of remote sensing images is a fundamental task in geospatial research. However, widely used Convolutional Neural Networks (CNNs) and Transformers have notable drawbacks: CNNs may be limited by insufficient remote sensing modeling capability, while Transformers face challenges due to computational complexity. In this paper, we propose a remote-sensing image semantic segmentation network named LKASeg, which combines Large Kernel Attention(LSKA) and Full-Scale Skip Connections(FSC). Specifically, we propose a decoder based on Large Kernel Attention (LKA), which extract global features while avoiding the computational overhead of self-attention and providing channel adaptability. To achieve full-scale feature learning and fusion, we apply Full-Scale Skip Connections (FSC) between the encoder and decoder. We conducted experiments by combining the LKA-based decoder with FSC. On the ISPRS Vaihingen dataset, the mF1 and mIoU scores achieved 90.33% and 82.77%.

本研究针对遥感图像语义分割中的传统卷积神经网络（CNN）和变换器在建模能力和计算复杂性方面的局限，提出了一种名为LKASeg的网络。该方法结合了大核注意力和全尺度跳跃连接的优点，实现了高效的全尺度特征学习与融合，并在ISPRS Vaihingen数据集上取得了卓越性能，mF1和mIoU分数分别达到90.33%和82.77%。

LKASeg：结合大核注意力和全尺度跳跃连接的遥感图像语义分割