Thermal background modeling and its use in science and technology

Wollenweber, Fritz G.

doi:10.1117/12.137873

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Different aspects of how to handle the influence from the vegetation could be read in [22][23][24]. The result from the presented model does not handle the thermodynamically behavior of the vegetation very well compared to the measured temperature by the pt-100 sensor placed on the surface in the grass.…”

Section: Numerical Modellingmentioning

confidence: 98%

Temporal method for IR minefield feature detection

Sjökvist

Linderhed

Nyberg

et al. 2004

Detection and Remediation Technologies for Mines and Minelike Targets IX

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The overall objective of this work is to investigate the possibilities of using airborne IR sensors for the purpose of detecting minefield features, such as land mines. A method is proposed for temporal analysis by extracting relevant information from diurnal IR images utilizing a combination of thermodynamic modelling, signal and image processing. This paper presents results from a field test of level 2 survey in May 2003 of suspected mine-polluted areas in Croatia. Airborne data was acquired using an IR sensor mounted on a rotary wing UAV. A weather station was used to collect weather data, and pt-100 temperature sensors recorded the temperature gradient in the soil and in reference markers that were used for calibrating the IR camera. The proposed method compares simulated temporal temperature with image data collected at several times during a diurnal cycle from the same area, pixel by pixel. The images are co-registered and calibrated with respect to reference values. The numerical model is based on physical laws and is set with relevant properties, geometries, materials, surface coefficients and the influence of the actual weather sets the boundary conditions. This paper shows some results from using temporal features for detection of different relevant objects in a real minefield.

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Section: Numerical Modellingmentioning

confidence: 98%