Detecting coal fires with a neural network to reduce the effect of solar radiation on Landsat Thematic Mapper thermal infrared images

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Abstract:

Coal fires in the north of China have already resulted in serious problems, including huge losses in coal resources, air pollution and so on. Thermal infrared images by Landsat Thematic Mapper (TM) can be used to detect some thermal anomalies. However, an initial necessity is to reduce the effect of solar radiation on TM thermal infrared images. In this paper, a neural network is used to set up a mathematical model of ground temperature for the first time. After the neural network completes training, we can use it to calculate the ground temperature caused by solar radiation. Thus, the result can be used to reduce the effect of solar radiation on TM thermal infrared images, and extract the thermal anomalies caused by coal fires.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/014311601300074469

Affiliations: 1: Department of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China 2: Aerophotogrammetry and Remote Sensing Center of China Coal, Xi'an, Shaanxi 710054, China

Publication date: April 20, 2001

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