Please wait a minute...


Remote Sensing Technology and Application  2000, Vol. 15 Issue (3): 146-150    DOI: 10.11873/j.issn.1004-0323.2000.3.146
Detecting the Coal Fires on Landsat TM Thermal IR Images with Neural Network
DENG Wei1,WAN Yu-qing2,ZHAO Rong-chun3
(1.Department of Computer Science and Engineering,Northwestern Polytechnical University,
Xi'an710072,China;2.Aerophotogrammetry and Remote Sensing of China Coal,Xi'an710054,China;3.Department of Computer Science and Engineering,Northwestern Polytechnical University,Xi'an710072,China)
Download:  PDF (0KB) 
Export:  BibTeX | EndNote (RIS)      

Coal fires are widely prevalent in the north of China. They have already caused huge losses in
resources and pose a serious environmental problem. To monitor and extinguish coal fires, the first step is
to detect their location and scale. Because of the huge amounts of heat energy released by coal fires, the
resulting thermal anomalies can be detected by using thermal infrared remote sensing technology. On
nocturnal aerial images it is relatively easy to discern coal fires, because the effect of solar radiation is
insignificant. However, nocturnal aerial images are not available as often as Landsat TM daytime images
for such a large area as the north of China. In this paper, we first give a briefing of the basic principle in
reducing solar radiation on TM thermal IR image. Then, neural network is used to set up a mathematical
model of ground temperature. In view of the special character of artificial neural network used in this
application, we offer the batch learning approach and adjust active momental factor. The result achieved
by reducing solar radiation on TM thermal IR image is as good as airborne nighttime thermal infrared
image for detecting coal fires. So this method is very practical and greatly economizes the cost of aerial
remote sensing image.

Key words:  Remote sensing image processing      Thermal anomaly extraction      Neural network     
Received:  20 April 2000      Published:  23 February 2012
TP 751.1   
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
Articles by authors

Cite this article: 

DENG Wei,WAN Yu-qing,ZHAO Rong-chun. Detecting the Coal Fires on Landsat TM Thermal IR Images with Neural Network. Remote Sensing Technology and Application, 2000, 15(3): 146-150.

URL:     OR

〔1〕 Bartolucci L A.Atmospheric Effects on Landsat TM Thermal IR Data〔J〕. IEEE Transaction on Geo-science and Remote Sensing,1988,26:171~175.
〔2〕 Norman J M, Divakarla M, Goel N S. Algorithms for Extraction from Remote Thermal IR Observation of the
Earth' s Surface〔J〕. Remote Sensing of Environment,1994,12:331~348.
〔3〕 Mannstein H.Land Surface Energy Budget〔J〕. NATO ASI Series,1994,124:367~391.
〔4〕 Dave J V.Effect of Terrain Orientation and Solar Position on Satellite-level Luminance Observation〔J〕.Remote Sensing of Environment,1982,12:331~248.
〔5〕 杨凯.遥感图像处理原理与方法〔M〕.北京:测绘出版社,1988.
〔6〕 Rumelhart D E,Mcclelland J L,The PDP Research Group. Parallel Distributed Processing: Exploration in Microstructure of Cognition〔Z〕.vol. 1: Foundations (Cambridge, MA: The MIT PRESS), 1986.

No Suggested Reading articles found!