Classification of leakage detections acquired by airborne thermography of district heating networks
2014 (English)In: 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE , 2014, 1-4 p.Conference paper (Refereed)
We address the problem of reducing the number offalse alarms among automatically detected leakages in districtheating networks. The leakages are detected in images capturedby an airborne thermal camera, and each detection correspondsto an image region with abnormally high temperature. Thisapproach yields a significant number of false positives, and wepropose to reduce this number in two steps. First, we use abuilding segmentation scheme in order to remove detectionson buildings. Second, we extract features from the detectionsand use a Random forest classifier on the remaining detections.We provide extensive experimental analysis on real-world data,showing that this post-processing step significantly improves theusefulness of the system.
Place, publisher, year, edition, pages
IEEE , 2014. 1-4 p.
, IAPR Workshop on Pattern Recognition in Remote Sensing, ISSN 2377-0198
IdentifiersURN: urn:nbn:se:liu:diva-110046DOI: 10.1109/PRRS.2014.6914288ISI: 000363273500012ISBN: 978-1-4799-7276-0OAI: oai:DiVA.org:liu-110046DiVA: diva2:776248
8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), Stockholm, Sweden, 24-24 Aug. 2014