Thermal Power Plant Detection in Remote Sensing Images With Saliency Enhanced Feature Representation

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is challenging due to the variations in appearance and complex structures. In this article, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, a large-scale TPPs Dataset for Detection (AIR-TPPDD) in remote sensing images is presented. AIR-TPPDD is collected from the Google Earth worldwide, and provides detailed annotations including names and locations. To the best of our knowledge, this is the first publicly available dataset for TPP detection. Then, based on Faster R-CNN, a saliency enhanced module is proposed to strengthen the ability in representing complex structure, as well as alleviate distractions in the background. In addition, we design a multi-scale feature module to adapt to the large size range of TPPs. Experiments show that the proposed method outperforms the state-of-the-art methods and achieves 76.7% mAP on the challenging AIR-TPPDD.