Thermal Infrared Satellite Imagery Resolution enhancement with Fuzzy Logic Bandpass filtering

Satellite remote sensing long-wave infrared data is valuable for wide range applications in the environment protection, fire monitoring, as well as for the formation of thermal fields' dynamic time series. Due to physical, technological and design constraints, spatial resolution of these data is relatively low. This one restricts detailization and informativity of resulting cartographic data. Some known of spatial resolution enhancement approach is based on subpixel-shifted temperature distribution image pairs' processing within the frequency domain separately for each frequency components. The main problem is the correct extraction of individual frequency components responsible for different physical entities in source infrared image. Discrete Fourier transform (DFT) is traditionally used for data conversion from spatial/temporal to frequency domain. After task-oriented frequency component processing it is possible to restore enhanced spatial data by inverse DFT.