SP-RAN: Self-Paced Residual Aggregated Network for Solar Panel Mapping in Weakly Labeled Aerial Images

With the rapid development of the solar distribution, solar panel mapping is becoming increasingly valuable to decision-makers. Weakly supervised methods have been developed to reduce the cost in training sample collection, and the most successful ones follow the alternative training scheme, which first generates coarse object localizations as pseudo labels (PLs) and then utilizes these PLs to train an end-to-end network for object extraction. As remote sensing images are typically characterized by multiple occurrences of objects and complicated backgrounds, the alternative training scheme suffers from low mapping accuracy and deficient boundary maintenance due to the varying quality of PLs. In this article, we focus on addressing these problems by adaptively adjusting the contributions of quality-varying PLs and propose a novel self-paced residual aggregated network (SP-RAN) for solar panel mapping. Specifically, with the initial PLs generated by gradient-weighted class activation mapping, a residual aggregated network is designed for target mapping with special consideration for the capability in producing complete and well-shaped mapping results. Considering the inconsistent quality of PLs, an effective confidence-aware (CA) loss is developed to emphasize the contribution of high-quality PLs and alleviate the negative impacts brought by the bad-quality ones in the training phase. Moreover, to concentrate on boundary maintenance, a novel self-paced label correction (SP-LC) strategy is proposed to selectively update PLs by considering their reliability. Extensive experimental comparisons with state-of-the-art methods and ablation study on two aerial datasets and a remote sensing dataset demonstrate the superiority of the proposed method.