Thermal Anomaly Detection of Industrial Zones with MNF and ICA

Year 2021, Volume: 2 Issue: 1, 18 - 26, 15.06.2021

Enis Arslan

https://doi.org/10.48053/turkgeo.811887

Abstract

Thermal anomalies can be detected with the help of the imagery provided by the satellite systems such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). ASTER provides five thermal bands for the effective analysis of thermal anomalies. In order to achieve this goal, considering the physical phenomena, many satellite signal processing methods and algorithms can be used. In this study, depending on the studied area, heat characteristics and extent are presented by using four days of data from daytime and nighttime scenes. In order to define the thermal anomalies for the studied area, Land Surface Temperature (LST) was estimated by inverse Planck function approach for all TIR bands. Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA) methods were applied on all thermal infrared (TIR) bands. The results of MNF and ICA components show location of the thermal anomalies for industrial complexes especially in nighttime scenes.

Keywords

Land surface temperature, Thermal anomaly, Minimum noise fraction, Independent component analysis

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