In February 2018 Australia and Norway jointly conducted a field trial in Darwin collecting IR imagery in adverse weather conditions. The wet season in the Northern Territory is charactersied by high temperatures and humidity with intensive rains, storms and cyclones. The monsoon conditions subsided early February, but the collected data still included the required variety of atmospheric conditions. Two fully instrumented small boats performed a set of pre-designed manoeuvres and data was collected throughout the diurnal cycle. DST team used FLIR long-wave and mid-wave IR cameras. Weather data (temperature, humidity, barometric pressure, wind speed and direction) was also locally collected for the duration of the trial.
The purpose of this paper is to present aspects of modelling of elements of IR scenes using the DST-developed VIRSuite tool (Virtual Infrared Simulation). Modelling will focus on mid-wave IR rendition and direct comparison with the collected imagery.
A joint Australian-Norwegian field trial (Osprey) was held in February 2018 in Darwin, Australia. The objective of this trial was to measure IR transmission properties of the atmosphere in a marine environment under warm and humid conditions. Darwin is in the tropics (longitude 12° south), and February is the middle of the "wet season". Various temperature-controlled sources (blackbodies) were used during the trial. Land based weather stations recorded a number of meteorological data. The sensors used in the trial included long-wave, mid-wave and short-wave IR cameras. In this paper we present the analysis of measurements performed on two blackbodies across Darwin Harbour. The scene was recorded with an IRCAM LW camera and calibrated to blackbodies with known temperature. We have modelled the atmospheric transmittance using MODTRAN, and from this acquired the equivalent blackbody temperature of the scene. In our analysis, we are not only interested in the overall agreement between predictions and data, but also on the sensitivity of the predictions to uncertainties of the input parameters (calibration temperatures, air temperature, humidity, etc.). In order to study this sensitivity, we used variance based sensitivity analysis and Monte Carlo simulations to compute sensitivity indices, according to methods developed by Saltelli and others. Our main finding is that uncertainties in calibration parameters (blackbody and camera temperatures) give the dominant contributions to the error in the computed equivalent temperature.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.