Infrared (IR) cameras typically output single channel frames with 13-16 bit dynamic range. On the other hand, typical Commercial Off-The-Shelf (COTS) monitors can display three channel frames with only 8 bit dynamic range. Therefore, visualization of IR images while preserving details is a challenging task for utilizing the full potential of thermal cameras. In this paper, we propose a radiometry based method for converting 13-16 bit IR images to 8 bit single channel images for visualization purposes, which is called temperature quantizer. In the proposed method, we use the histogram of the calibrated temperature values. In this context, we collected data using an uncooled Long Wave IR (LWIR), a cooled Middle Wave IR (MWIR) and a cooled LWIR camera. The proposed method is compared with a basic min-max scaler and a Digital Number (DN) quantizer that uses raw image histogram. For quantitative comparison, we used standard deviation and entropy values. Experiments show that the proposed method performs better than the reference methods quantitatively and qualitatively.
A vehicle classification system, which uses features based on radiometry, is developed for single band infrared (IR) image sequences. In this context, the process is divided into three components. These are moving vehicle detection, radiance estimation, and classification. The major contribution of this paper lies in the usage of the radiance values as features, other than the raw output of IR camera output, to improve the classification performance of the detected objects. The motivation behind this is that each vehicle class has a discriminating radiance value that originates from the source temperature of the object modified by the intrinsic characteristics of the radiating surface and the environment. As a consequence, significant performance gains are achieved due to the use of radiance values in classification for the utilized measurement system.
Flares are used as valuable electronic warfare assets for the battle against infrared guided missiles. The trajectory of the flare is one of the most important factors that determine the effectiveness of the counter measure. Reconstruction of the three dimensional (3D) position of a point, which is seen by multiple cameras, is a common problem. Camera placement, camera calibration, corresponding pixel determination in between the images of different cameras and also the triangulation algorithm affect the performance of 3D position estimation. In this paper, we specifically investigate the effects of camera placement on the flare trajectory estimation performance by simulations. Firstly, 3D trajectory of a flare and also the aircraft, which dispenses the flare, are generated with simple motion models. Then, we place two virtual ideal pinhole camera models on different locations. Assuming the cameras are tracking the aircraft perfectly, the view vectors of the cameras are computed. Afterwards, using the view vector of each camera and also the 3D position of the flare, image plane coordinates of the flare on both cameras are computed using the field of view (FOV) values. To increase the fidelity of the simulation, we have used two sources of error. One is used to model the uncertainties in the determination of the camera view vectors, i.e. the orientations of the cameras are measured noisy. Second noise source is used to model the imperfections of the corresponding pixel determination of the flare in between the two cameras. Finally, 3D position of the flare is estimated using the corresponding pixel indices, view vector and also the FOV of the cameras by triangulation. All the processes mentioned so far are repeated for different relative camera placements so that the optimum estimation error performance is found for the given aircraft and are trajectories.
The infrared (IR) energy radiated from any source passes through the atmosphere before reaching the sensor. As a result, the total signature captured by the IR sensor is significantly modified by the atmospheric effects. The dominant physical quantities that constitute the mentioned atmospheric effects are the atmospheric transmittance and the atmospheric path radiance. The incoming IR radiation is attenuated by the transmittance and path radiance is added on top of the attenuated radiation. In IR scene simulations OpenGL is widely used for rendering purposes. In the literature there are studies, which model the atmospheric effects in an IR band using OpenGLs exponential fog model as suggested by Beers law. In the standard pipeline of OpenGL, the related fog model needs single equivalent OpenGL variables for the transmittance and path radiance, which actually depend on both the distance between the source and the sensor and also on the wavelength of interest. However, in the conditions where the range dependency cannot be modeled as an exponential function, it is not accurate to replace the atmospheric quantities with a single parameter. The introduction of OpenGL Shading Language (GLSL) has enabled the developers to use the GPU more flexible. In this paper, a novel method is proposed for the atmospheric effects modeling using the least squares estimation with polynomial fitting by programmable OpenGL shader programs built with GLSL. In this context, a radiative transfer model code is used to obtain the transmittance and path radiance data. Then, polynomial fits are computed for the range dependency of these variables. Hence, the atmospheric effects model data that will be uploaded in the GPU memory is significantly reduced. Moreover, the error because of fitting is negligible as long as narrow IR bands are used.