Azwitamisi E. Mudau

Non-uniformity correction and bad pixel replacement on LWIR and MWIR images

To fully exploit the potential of current generation infrared focal plane arrays, it is crucial to correct for the fixed pattern noise. This paper presents two-point non-uniformity corrections (NUC) applied to infrared images acquired with long wave and medium wave infrared cameras. Pixels that are not corrected by the NUC process, defined as bad pixels, were identified and replaced using a nearest neighbor algorithm.

Pyradi: an open-source toolkit for infrared calculation and data processing

Electro-optical system design, data analysis and modeling involve a significant amount of calculation and processing. Many of these calculations are of a repetitive and general nature, suitable for including in a generic toolkit. The availability of such a toolkit facilitates and increases productivity during subsequent tool development: “develop once and use many times”. The concept of an extendible toolkit lends itself naturally to the open-source philosophy, where the toolkit user-base develops the capability cooperatively, for mutual benefit. This paper covers the underlying philosophy to the toolkit development, brief descriptions and examples of the various tools and an overview of the electro-optical toolkit. The toolkit is an extendable, integrated collection of basic functions, code modules, documentation, example templates, tests and resources, that can be applied towards diverse calculations in the electro-optics domain. The toolkit covers (1) models of physical concepts (e.g. Planck’s Law), (2) mathematical operations (e.g. spectral integrals, spatial integrals, convolution, 3-D noise calculation), (3) data manipulation (e.g. file input/output, interpolation, normalisation), and (4) graphical visualisation (2-D and 3-D graphs). Toolkits are often written in scriptable languages, such as Python and Matlab. This specific toolkit is implemented in Python and its associated modules Numpy, SciPy, Matlplotlib, Mayavi, and PyQt/PySide. In recent years these tools have stabilized and matured sufficiently to support mainstream tool development. Collectively, these tools provide a very powerful capability, even beyond the confines of this toolkit alone. Furthermore, these tools are freely available. Rudimentary radiometric theory is given in the paper to support the examples given. Examples of the toolkit use, as described in the paper, include (1) spectral radiometric calculations of arbitrary source-medium-sensor configurations, (2) spectral convolution processing, (3) 3-D noise analysis, (4) loading of ASCII text files, binary files, Modtran tape7 and FLIR Inc *.ptw files, (5) data visualization in 2-D and 3-D graphs and plots, (6) detector modeling from detail design parameters (bulk material detectors), (7) color coordinate calculations, and (8) various utility functions. The toolkit is developed as a cooperative effort between the CSIR, Denel SOC and DCTA. The project, available on Google Code at http://code.google.com/p/pyradi, is managed in accordance with general practice in the open source community.

Future-proofing an aircraft self-protection IR signature database

Aircraft self-protection against heat seeking missile threats is an extremely important topic worldwide, recently even more so with the instability in the Middle East region due to, for example, the large number of man-portable air defense systems (MANPADS) that were stolen from army arsenals. A fundamental step in successfully achieving self-protection is the ability to capture and identify aircraft infrared signatures. This work discusses some of our efforts and results in creating an asset database for infrared signatures. The database was designed in a way that will feed an image processing engine to allow for automated feature and signature extraction. A common failing in the handling of target signature raw data is the fact that raw data files can become unreadable because of changes in technology, software applications or weak media archiving technology (e.g. corrupt DVD media). A second shortcoming is often the fact that large volumes of raw or processed data are stored in an unstructured manner, resulting in poor recall later. A third requirement is the portability of data between various processing software packages, legacy, current and future. This paper demonstrates how the challenge of future-proofing measured data is met with reference to the archiving and analysis of data from a recent measurement campaign. Recommendations for future work are given, based on the experience gained.

The pyradi radiometry toolkit

Modeling and designing in electro-optical systems entails the calculation of several (often interrelated) parameters. Many of these calculations are repetitive, suitable for including in a generic toolkit. A well-designed kit would facilitate work flow and increase productivity during the modeling and design process. The concept of an extendable toolkit lends itself naturally to the open-source philosophy, where users cooperatively develop new tools to add to an ever-expanding set for the mutual benefit of all. The pyradi toolkit is an extendable, integrated and coherent collection of basic functions that can be applied towards diverse calculations in the electro-optics domain.1 The name pyradi is derived from the combination of ‘Python’ and ‘Radiometry.’ We initially considered two candidate languages for pyradi, MATLAB and Python. MATLAB has a strong following in the scientific community. In recent years Python and associated modules have beenwell tested, and have stabilized andmatured sufficiently to support mainstream tool development. But after extensive use of both languages for radiometric calculation and modeling, we decided to continue only with Python. With this application and its features in mind, Python provides better capability as a general purpose language, and its data visualization tools, Matplotlib and Mayavi, are the most powerful available today. While the wider range of toolboxes (including Simulink) might be a compelling reason to use MATLAB, those capabilities are not required here. We have already designed a number of pyradi modules covering basic electro-optical system calculations. For instance, the ryplanck module provides functions for Planck Law emittance calculations, as well as the Planck Law temperature derivative functions. Given the temperature and spectral vector, the functions provide spectral emittance in W/(m2 ) or q/(s m2 ), with spectral variable * in wavelength, Figure 1. Example polar plots: note axes conventions and yellow/red highlight of negative values.

Vicarious calibration campaign in Argentina for radiometric calibration of a multispectral imager onboard Sumbandila Satellite

Continuous assessment of the radiometric response of Earth Observation (EO) satellite imagers is necessary for the quality assurance of derived data products. With the launch of South Africas SumbandilaSat in September 2009, a number of vicarious calibration campaigns have been planned and executed to meet this requirement for the high-resolution multispectral imager payload. This paper describes a vicarious calibration campaign executed in Argentina in October 2010. A number of salt pans in Argentina were visited and characterised during this campaign. SumbandilaSat images of two of the sites were captured simultaneously with in situ measurements.

Optronic Measurement, Testing and the Need for Valid Results: Example of Infrared Measurements for Defence Countermeasures

Abstract: In the development of defence products and applications, measurements serve a key role when characterising and verifying the behaviour and performance of components and systems. In addition, modelling and simulation depend heavily on measured data to achieve realistic predictions, and is furthermore dependent on measured data for validation. Researchers at the Optronic Sensor Systems (OSS) Competency Area perform field measurements in the infrared (IR) spectral band, which serve inter alia, for the design and optimisation of IR countermeasures, such as flares. An important requirement during field measurements is a deep scientific understanding of the measurement equipment, measurement processes or protocols, the test sample, and the effects of the environment on the measurement outcome. It is also important to understand and account for the influence of the environment on the measurement outcome. This paper briefly summarises the measurement procedure used during infrared flare measurement in the field and the use of reference measurements used to validate the instrument status, and hence, the measurement. Reference measurement results obtained from IR thermal imagers were compared to the results obtained using a handheld infrared thermometer in order to provide confidence in the results obtained by IR thermal imagers.