Related Experiment Video
Updated: Jul 6, 2026
07:13
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
4.3K
Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images
Chunming Han1,2, Guangfu Li1,2, Yixing Ding1
1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|August 9, 2020
Summary
Accurately detecting industrial chimneys from remote sensing images is crucial for environmental monitoring. This study combines Faster R-CNN with digital terrain model filtering and a main direction test to significantly improve chimney detection accuracy and reduce false positives.
Area of Science:
- Environmental Science
- Remote Sensing
- Computer Vision
Background:
- Industrial chimneys are key sources of atmospheric pollution, making their accurate identification and status monitoring vital for urban environmental governance.
- Existing methods for detecting chimneys in high-resolution remote sensing images often suffer from high false positive rates, hindering effective environmental monitoring.
