Enhancing Photovoltaic Panel Segmentation in Remote Sensing Imagery: A Comparative Study of Attention-Integrated UNet Models

Abstract. This study explores the enhancement of UNet-based semantic segmentation for photovoltaic (PV) panels in remote sensing images by integrating attention mechanisms. Given the critical role of solar energy in achieving global sustainability, accurate PV panel detection is essential for effective energy management. Using the high-resolution PV01 dataset, which includes UAV-captured rooftop PV samples, we evaluate the impact of four distinct attention modules: Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation Networks (SE-Net), Efficient Channel Attention (ECA-Net), and Coordinate Attention (CA) on segmentation performance. Comparative analysis demonstrates that UNet models with SE and CA modules substantially outperform the baseline, achieving the highest scores in Average Accuracy (AA), Average Precision (AP), Average Recall (AR), mean Intersection over Union (mIoU), and Average F1-score (AF). In particular, UNet + SE achieved an AA of 0.9809, AP of 0.9756, AR of 0.9629, AF of 0.9692, and mIoU of 0.9403, highlighting the efficacy of attention mechanisms in refining feature representations and advancing PV panel segmentation, thereby contributing to large-scale solar energy monitoring and deployment.