Improving Object-Oriented Small Chimney Detection Performance in High-Resolution Satellite Imagery via Targeted Augmentation Strategies

Chimneys play a crucial role in monitoring industrial emissions and assessing air pollution. While large chimneys are easily identifiable in high-resolution satellite imagery, small chimneys pose unique challenges due to their size, arbitrary orientations, and tendency to blend into dense urban environments. Further complications arise from inconsistent annotation practices in open-source datasets, where condensing towers are labeled as chimneys, and from intra-class imbalance resulting from the underrepresentation of clustered chimneys. These clustered chimneys typically appear in parallel arrangements, and their close spatial proximity leads to non-occluded overlapping bounding boxes, hindering the model’s ability to distinguish object boundaries. To address these issues, this study proposes a large chimney filtering technique based on the statistical scale distribution of chimney sizes to effectively remove condensing tower annotations. Additionally, several targeted data augmentation strategies are introduced to enhance model generalization. A comparative evaluation of six oriented object detection models was conducted. For robustness assessment, an inference dataset containing small chimneys was constructed and cross-validated using Google Earth imagery to ensure annotation accuracy. Experimental results on the artificial intelligence (AI)-Hub, BUAA-FFPP60, and DIOR-R open-source datasets demonstrate that the proposed method significantly improves small chimney detection in complex urban environments, achieving an average precision of 0.907 on the open-source datasets and 0.523 on the inference dataset. These results show good potential for improving air pollution monitoring.