In this work, we studied how detection range of a small target changes as a function of the number of defined target pixels. The intuitively simplest method to look for a target in IR images is to look for the “hottest” (highest intensity) pixel. Image noise or sun glint may cause false detection. To build in some robustness against such detractions, one considers looking for the hottest group (blob) of contiguous pixels. A blob could be any shape; here we consider square blobs, 1x1, 2x2, 3x3, etc. pixels. One expects the average blob intensity to decrease with blob size. On the other hand, the noisiness of the background blobs also decreases with blob size. The net result is that initially, for small blob sizes, the detection range increases before falling again for larger blob sizes. We demonstrate this by analyzing IR recordings of a small vessel sailing outbound until it “disappears”. We develop a simple model that supports these observations. The model is based on a synthetically generated sequence of images of a receding target, uses a basic sensor characteristic, and Johnson’s detection criterion.
We applied a simple method to estimate the Minimum Resolvable Temperature Difference (MRTD) of an LWIR and an MWIR camera. A so-called Siemens star, in our case a thin, black aluminum plate framing a circle that is missing (cut out) every other spoke, is mounted in front of a black body whose temperature is relatively close to room temperature. From short recordings of the black body and Siemens star both the Noise Equivalent Temperature Difference (NETD) and the Modulation Transfer Function (MTF) are extracted and a simple estimate of MRTD = NETD/MTF is obtained. The imaged Siemens star almost completely covers the focal plane array; hence, an MRTD curve for the whole array is obtained. We investigated the effect of Non-Uniformity Correction (NUC) and Bad-Pixel Removal (BPX), two often applied pre-processing techniques, on the MRTD estimate. We find that (1) BPX has only limited effect on the result; (2) NUC is required to obtain a good MTF; and (3) NUC is not a prerequisite to obtain a good NETD estimate, but this is contingent on having a proper segmentation tool or template available. Without a segmentation algorithm, NUC together with simple intensity thresholding provides a sufficiently good segmentation and accordingly a good estimate of NETD.
The three-dimensional noise model is a methodology to analyse the noise of a thermal imaging sensor, such as an infrared (IR) camera. This allows us to decompose a noisy signal into components and quantify properties such as noise equivalent temperature difference (NETD), temporal noise, rain, streaks, or various types of fixed pattern noise. As part of this analysis, it is necessary to identify trends in order to split the data into signal and noise. In this paper we discuss methods to perform this split. We then show that not only the noise, but also the trends contain interesting information and can be used to quantify large-scale non-uniformities in calibrated IR images. We apply this analysis to investigate three different effects that may appear in recorded data: How does the uniformity of the background change when we vary the temperature, the distance, or the lens focus? We have performed a series of laboratory measurements on blackbodies in order to investigate these effects. We find that large-scale non-uniformity may be present even in calibrated images, with an order of magnitude up to ΔT~0:6 K.
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.
We present Minimum-Resolvable Temperature Difference (MRTD) curves obtained by letting an ensemble of observers
judge how many of the six four-bar patterns they can “see” in a set of images taken with different bar-to-background
contrasts. The same images are analyzed using elemental signal analysis algorithms and machine-analysis based MRTD
curves are obtained. We show that by adjusting the minimum required signal-to-noise ratio the machine-based MRTDs
are very similar to the ones obtained with the help of the human observers.
A long term field trial called FESTER (First European South African Transmission Experiment) has been conducted by an international collaboration of research organizations during the course of almost one year at False Bay, South Africa. Main objectives of the experiment are a better insight into atmospherical effects on propagation of optical radiation, a deeper understanding of the effects of (marine) aerosols on transmission, and the connection of the mentioned effects to the general meteorological and oceanographic conditions/parameters. Modelling of wakes and possible infrared-radar synergy effects are further points of interest. The duration of one year ensures the coverage of most of the relevant meteorological conditions during the different seasons. While some measurements have been performed by permanent installations, others have been performed during intensive observation periods (IOP). These IOPs took place every two to three months to ensure seasonal changes. The IOPs lasted two weeks. We will give an overview of the general layout of the experiment and report on first results. An outlook on the planned analysis of the acquired data, which includes linkage to the Weather Research and Forecasting model (WRF), will be given.
An overview is given of the First European – South African Transmission ExpeRiment (FESTER), which took place in South Africa, over the False Bay area, centered around Simon’s Town. The experiment lasted from April 2015 through February 2016 and involved continuous observations as well as periodic observations that took place during four Intensive Observation Periods (IOPs) of 2 weeks each, which were spread over the year. The continuous observations aimed at a characterization of the electro-optical propagation environment, and included standard meteorology, aerosol, refraction and turbulence measurements. The periodic observations aimed at assessing the performance of electro-optical sensors in VIS / SWIR / MWIR and LWIR wavebands by following a boat sailing outbound and inbound tracks. In addition, dynamic aspects of electro-optical signatures, i.e., the changes induced by variations in the environment and/or target orientation, were studied. The present paper provides an overview of the trial, and presents a few first results.
In the past decades the Norwegian Defence Research Establishment (FFI) has recorded and characterized infrared
scenarios for several application purposes, such as infrared target and background modeling and simulation, model
validation, atmospheric propagation, and image segmentation and target detection for civilian and defence purposes.
During the last year FFI has acquired several new systems for characterization of infrared radiation properties. In total,
five new infrared cameras from IRCAM GmbH, Germany, have been acquired. These cameras cover both the longwavelength
and extended medium-wavelength infrared spectral bands. The cameras are equipped with fast rotating filter
wheels which can be used to study spectral properties and polarization effects within these wavelength bands. This
option allows the sensors to operate in user-defined spectral bands. FFI has also acquired two HyperCam sensors from
Telops Inc, Canada, covering the long-wavelength and extended medium-wavelength spectral bands, respectively. The
combination of imaging detectors and Fourier Transform spectroscopy allows simultaneous spectral and spatial
characterization of infrared scenarios. These sensors may optionally be operated as high-speed infrared cameras. A
description of the new sensors and their capabilities are presented together with some examples of results acquired by the
different sensors. In this paper we present a detailed comparison of images taken in different spectral bands, and also
compare images taken with the two types of sensors. These examples demonstrate the principles of how the new spectral
information can be used to separate certain targets from the background based on the spectral information.
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