Due to its highly destructive nature and significant disaster risk, accurate lightning monitoring is a crucial component of disaster prevention and mitigation. Geostationary meteorological satellites, such as Himawari-8, provide an ideal platform for lightning monitoring with the advantages of broad coverage and continuous observation. However, the physical correlation mechanism between satellite cloud images and lightning activity remains unclear, which restricts practical application. This study proposes the AE-UNet model, which utilizes Himawari satellite data and VLF long-range lightning detection network data, identifying high-density lightning areas from satellite cloud images. The AE-UNet model incorporates a channel attention mechanism and residual connections to adaptively and deeply fuse the multi-channel satellite features. During the concatenation of multi-scale features, a channel-space dual attention mechanism is embedded to fully explore spatial correlations. The experimental results show that the AE-UNet model achieves a lightning identification accuracy of 97.91%, a probability of detection (POD) of 67.47%, and a false alarm rate (FAR) of 26.92%, demonstrating significant performance improvement over the benchmark model. The proposed model can provide reliable lightning activity information based on satellite cloud images, thereby strongly supporting disaster prevention and mitigation efforts.