@inproceedings{d71bee66441b46488090999d534e38b2,
title = "Chimney and condensing tower detection based on faster R-CNN in high resolution remote sensing images",
abstract = "The persistent haze weather in North China has aroused extensive attention to environmental protection. Among all pollution resources, the anthropogenic emission by fossil fuel power plants plays an important role. To assist the environmental protection administration monitoring fossil fuel power plants, we propose an effective approach in this paper to learn an integrated model for chimney and condensing tower detection based on Faster R-CNN in high resolution remote sensing images. Our method can detect chimneys and condensing towers under different imaging condition efficiently and accurately. Experimental results on a self-collected dataset demonstrate the effectiveness of the proposed method.",
keywords = "Faster R-CNN, Object detection, chimney, condensing tower, remote sensing images",
author = "Yuan Yao and Zhiguo Jiang and Haopeng Zhang and Bowen Cai and Gang Meng and Deshan Zuo",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",
year = "2017",
month = dec,
day = "1",
doi = "10.1109/IGARSS.2017.8127710",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3329--3332",
booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",
address = "美国",
}