Jonathan M. Mooney

Measurement of the modulation transfer function of infrared cameras

The performance of starting PtSi infrared cameras is characterized based on estimating their spatial frequency response. Applying a modified knife-edge technique, we arrive at an estimate of the edge spread function (ESF), which is used to obtain a profile through the center of the 2-D modulation transfer function (MTF). Using this technique, the complete system MTF in the horizontal and vertical direction is measured for various imaging systems. The influence of charge transfer efficiency (CTE) on the knife-edge measurement and resulting MTF is also modeled and discussed. An estimate of the OlE can actually be obtained from the shape of the ESF in the horizontal direction. In addition, we demonstrate that this technique can be used as a field measurement. By applying the technique at long range, the MTF of the atmosphere can be measured.

Responsivity Nonuniformity Limited Performance Of Infrared Staring Cameras

Abstract. The effect of noise, including system noise, background noise, and cell-to-cell nonuniformity (spatial noise), is mathematically treated and experimentally verified for staring-mode infrared cameras. Spatial noise is shown to be dominant in high background environments (3 to 5 Am or 8 to 12 Am imagery at background temperatures greater than 0°C) even after compensation. Camera sensitivity is quantified by a contrast signal-to-noise ratio that includes the effects of system, background, and spatial noise. Past analysis of camera performance has assumed that the cell-to-cell nonuniformity can be completely removed by using nonuniformity correction techniques. We present data showing that neither variations in detector spectral response nor excess low frequency noise can be fully corrected using existing nonuniformity correction techniques. Furthermore, we show that even the small amounts of nonuniformity that persist after the application of correction algorithms will significantly degrade camera performance.

Optimization of point target tracking filters

We review a powerful temporal-based algorithm, a triple temporal filter (TTF) with six input parameters, for detecting and tracking point targets in consecutive frame data acquired with staring infrared (IR) cameras. Using an extensive data set of locally acquired real-world data, we used an iterative optimization technique, the Simplex algorithm, to find an optimum set of input parameters for a given data set. Analysis of correlations among the optimum filter parameters based on a representative subset of our database led to two improved versions of the filter: one dedicated to noise-dominated scenes, the other to cloud clutter-dominated scenes. Additional correlations of filter parameters with measures of clutter severity and target velocity as well as simulations of filter responses to idealized targets reveal which features of the data determine the best choice of filter parameters. The performance characteristics of the filter is detailed by a few example scenes and metric plots of signal to clutter gains and signal to noise gains over the total database.

Characterizing IR FPA nonuniformity and IR camera spatial noise

Abstract In this paper we survey the methods used to characterize infrared focal plane array (IR FPA) nonuniformity and infrared camera spatial noise. Techniques are discussed which range from simple visual inspection of the video imagery to array characterization criteria which are computationally intensive. While the effect of spatially correlated noise patterns on sensor performance is impossible to quantify in a simple way, rather simple expressions can be used to obtain a representative assessment of sensor performance. We conclude that the temporal stability of the spatial noise pattern is most relevant to camera performance and that there is no simple parameter, such as D ∗ , which can predict sensor performance when spatial noise is present in the imagery. We also conclude that spatial noise must be compensated an order of magnitude below temporal noise for artifact free imagery.

Point target detection in consecutive frame staring infrared imagery with evolving cloud clutter

The problem of detection of aircraft at long range in a background of evolving cloud clutter is treated. A staring infrared camera is favored for this application due to its passive nature, day/night operation, and rapid frame rate. The rapid frame rate increases the frame-to-frame correlation of the evolving cloud clutter; cloud-clutter leakage is a prime source of false alarms. Targets of opportunity in daytime imagery were used to develop and compare two algorithm approaches: banks of spatio-temporal velocity filters followed by dynamic-programming-based stage-to-stage association, and a simple recursive temporal filter arrived at from a singular-value decomposition analysis of the data. To quantify the relative performance of the two approaches, we modify conventional metrics for signal-to-clutter gains in order to make them more germane to consecutive frame real data processing. The temporal filter, in responding preferentially to pixels influenced by moving point targets over those influenced by drifting clouds, achieves impressive cloud-clutter suppression without requiring subpixel frame registration. The velocity filter technique is roughly half as effective in clutter suppression but is twice as sensitive to weak targets in white noise (close to blue sky conditions). The real-time hardware implementation of the temporal filter is far more practical.

Temporal filters for tracking weak slow point targets in evolving cloud clutter

Abstract A class of temporal filters is presented for use with a staring infrared camera in detecting and tracking weak point targets moving slowly in evolving cloud clutter. The generic temporal filter, originally suggested by the singular value decomposition of consecutive frame data, is a zero mean damped sinusoid which can be recursively implemented in the complex plane. From this filter type, a composite triple temporal filter (TTF) is developed, consisting of two sinusoids of different periods in sequence followed by a third (averaging) filter. The TTF achieves impressive cloud clutter suppression by responding strongly to pixel temporal responses caused by moving point targets and weakly to responses caused by cloud edges moving into or out of pixels. An extensive database of local airfield scenes with targets of opportunity taken with two laboratory staring IR cameras was used in the design and testing of the filters. Issues and trade-offs in choosing the parameters of the TTF are explored by comparing two specific forms of the filter: the first based on a damped sinusoid with a period of 16 frames followed by one with a 10 frame period; the second filter has corresponding periods of 40 followed by 30 frames. The first TTF is very effective with targets having velocities from 0.1–0.5 pixels/frame in daytime drifting cloud scenes. However, target signal-to-noise values of ⩾6 are required for detection in white noise (close to blue-sky conditions). The second TTF is more sensitive to slower, weaker targets in blue-sky or cloudless night scenes; however, in order to operate in daytime cloud scenes, spatial enhancements are required. Results are detailed for some representative scenes and given as well for the total database as signal-to-clutter gain plots based on a newly formulated antimedian metric.

Evaluation of a PtSi infrared camera

A 160x244 element PtSi IRCCD imaging array is characterized using a conventional approach commonly reported for visible imagers, introducing, as needed, issues specific to the IR. Mean-variance data are used to extract two CCD charge transfer efficiencies; one efficiency corresponds to the charge partitioning in a transfer (0.9987), and the other efficiency corresponds to the charge lost to charge pumping in a transfer (0.9994). The array is shown to be background limited for pixels near the output node. A 2-point correction is shown to substantially reduce fixed pattern noise of linear PtSi photodetectors at backgrounds offset from the points of correction, but it can introduce additional noise at the point of compensation if adequate precision is not used when calculating the 2nd-point coefficients. The measured D* (detectivity) and NE?T (noise equivalent temperature difference) are 6.5x1010 cm. ?Hz W-1 and 0.1°C, respectively. The horizontal and vertical MRT (minimum resolvable temperature) of the array is measured to be 0.02°C at a spatial frequency of 1//6 cycle/mrad. Pixel 1/f noise was below shot noise to 2x 10_5 Hz.

Point target detection in consecutive frame staring IR imagery with evolving cloud clutter

We treat the problem of long range aircraft detection in the presence of evolving cloud clutter. The advantages of a staring infrared camera for this application include passive performance, day and night operation, and rapid frame rate. The latter increases frame correlation of evolving clouds and favors temporal processing. We used targets of opportunity in daytime imagery, which had sub-pixel velocities from 0.1 - 0.5 pixels per frame, to develop and assess two algorithmic approaches. The approaches are: (1) banks of spatio-temporal velocity filters followed by dynamic programming based stage-to-stage association, and (2) a simple recursive temporal filter suggested by a singular value decomposition of the consecutive frame data. In this paper, we outline the algorithms, present representative results in a pictorial fashion, and draw general conclusions on the relative performance. In a second paper, we quantify the relative performance of the two algorithms by applying newly developed metrics to extensive real world data. The temporal filter responds preferentially to pixels influenced by moving point targets over those influenced by drifting clouds and thus achieves impressive cloud clutter suppression without requiring sub-pixel frame registration. It is roughly twice as effective in clutter suppression when results are limited by cloud evolution. However when results are limited by temporal noise (close to blue sky conditions), the velocity filter approach is roughly twice as sensitive to weak targets in our velocity range. Real-time hardware implementation of the temporal filter is far more practical and is underway.

Display of wide dynamic range infrared images from PtSi Schottky barrier cameras

Twelve-bit digitized images taken with PtSi Schottky barrier detector arrays have been processed on Sun work stations. Two techniques for 8-bit global display are compared: the standard method of histogram equalization and a newly devised technique of histogram projection. The latter assigns equal dynamic range to each occupied level, while the former does so according to the density of the occupied levels. The projection technique generally gives distinctly superior results based on an extensive set of indoor, outdoor, day, and night imagery. For cases in which the two algorithms have complementary advantages, the techniques can be combined in effect by a weighting of their distribution functions, which often gives the desirable features of each. The new projection algorithm also can be used as a powerful and robust local contrast enhancement technique. An alternative method of contrast enhancement, a global algorithm based on modular (sawtooth) displays, affords a comparable degree of enhancement at less computational cost.

PtSi Internal Photoemission; Theory And Experiment

The process of internal photoemission is analyzed from both a theoretical and an experimental point of view. A recent model of the internal photoemission process is compared with measured data. The comparison lends credence to the model and yields results which can be used to optimize the performance of Schottky barrier infrared detectors.