Enhancing PV panel segmentation in remote sensing images with constraint refinement modules

Incorrect predictions or underestimation of a city's solar potential can result from neglecting common features of photovoltaic (PV) panels from remote sensing images. This paper proposes an improved approach to address the challenge of accurately segmenting PV panels from remote sensing images using deep learning methods. The proposed method incorporates common features of PV panels and a constraint refinement module (CRM) to perform the localization of PV panel regions and shape regularization more accurately. Specifically, the method uses a color loss function based on prior knowledge of color to refine the predicted region with correct color information among confusing objectives, and a shape loss function based on multi-layer shape targets calculation to refine the initial segments and constrain the edge information of predicted regions. Different CRMs are embedded into the four refined initial segment modules, respectively, to improve the detection IoU of PV panels by up to 7.44%. The best CRM-integrated model performs the best IoU of 74.66% when segmenting PV panels. The proposed method has important implications for urban PV panel segmentation at the city level and provides a promising solution for remote sensing image-based PV plate segmentation tasks in challenging scenarios.