当前，电磁频谱资源的高效利用是无线通信领域的热点问题，而电磁频谱地图（electromagnetic spectrum map, EMSM）能够可视化展示某任务区域内的频谱使用情况，为无线网络优化工作提供有效支持。针对场景复杂且空间点位监测数据有限条件下生成细粒度EMSM难度大的问题，提出了一种增强空间注意力特征块（enhanced spatial-attention feature block, ES-AFB）的改进深度残差网络（deep residual networks, DRN）模型，借鉴图像超分辨率思想并利用EMSM的强空间特性，设计深度残差网络提取EMSM的相关性和频谱特征，利用增强空间注意力特征块挖掘粗粒度EMSM的内在隐含空间特征，再通过网络的多层上采样模块重构数据尺寸，从而达到更好的细粒度图像恢复效果，能够利用有限的粗粒度监测数据生成高质量的细粒度EMSM。仿真实验结果验证了算法的有效性，利用实测数据生成EMSM的均方根误差不超过3%。

The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network's multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.