Detection performance of LWIR passive standoff chemical agent sensors is strongly influenced by various scene parameters, such as atmospheric conditions, temperature contrast, concentration-path length product (CL), agent absorption coefficient, and scene spectral variability. Although temperature contrast, CL, and agent absorption coefficient affect the detected signal in a predictable manner, fluctuations in background scene spectral radiance have less intuitive consequences. The spectral nature of the scene is not problematic in and of itself; instead it is spatial and temporal fluctuations in the scene spectral radiance that cannot be entirely corrected for with data processing. In addition, the consequence of such variability is a function of the spectral signature of the agent that is being detected and is thus different for each agent. To bracket the performance of background-limited (low sensor NEDN), passive standoff chemical sensors in the range of relevant conditions, assessment of real scene data is necessary1. Currently, such data is not widely available2. To begin to span the range of relevant scene conditions, we have acquired high fidelity scene spectral radiance measurements with a Telops FTIR imaging spectrometer3. We have acquired data in a variety of indoor and outdoor locations at different times of day and year. Some locations include indoor office environments, airports, urban and suburban scenes, waterways, and forest. We report agent-dependent clutter measurements for three of these backgrounds.
We consider the problem of remotely identifying gaseous materials using passive sensing of long-wave infrared (LWIR) spectral features at hyperspectral resolution. Gaseous materials are distinguishable in the LWIR because of their unique spectral fingerprints. A sensor degraded in capability by noise or limited spectral resolution, however, may be unable to positively identify contaminants, especially if they are present in low concentrations or if the spectral library used for comparisons includes materials with similar spectral signatures. This paper will quantify the relative importance of these parameters and express the relationships between them in a functional form which can be used as a rule of thumb in sensor design or in assessing sensor capability for a specific task.
This paper describes the simulation of remote sensing datacontaining a gas cloud.In each simulation, the spectra are degraded in spectral resolution and through the addition of noise to simulate spectra collected by sensors of varying design and capability. We form a trade space by systematically varying the number of sensor spectral channels and signal-to-noise ratio over a range of values. For each scenario, we evaluate the capability of the sensor for gas identification by computing the ratio of the F-statistic for the truth gas tothe same statistic computed over the rest of the library.The effect of the scope of the library is investigated as well, by computing statistics on the variability of the identification capability as the library composition is varied randomly.
Longwave Infrared (LWIR) data sets collected from airborne platforms provide opportunities for study of atmospheric and surface features in the emissive spectral regime. The transfer of radiation for LWIR scenes can be formulated in a manner that allows recovery of the surface-leaving radiance (a result of atmospheric compensation). Using a forward radiative transfer model, a number of modifications to the atmospheric component of the scene can be made and applied to the surface-leaving radiance to predict sensor radiance that reflects a desired scenario. One such modification is the inclusion of a layer of effluent, the structure of which can be simulated by a plume model. Additionally, a different set of atmospheric conditions can be modeled and used to replace the conditions present in the scene. The resultant scene radiance field can be used to test algorithms for effluent characterization since the composition of the effluent layer and the intervening atmosphere is known. This approach allows for the embedding of a plume layer containing any combination of effluents from a set of over 400 gas spectra, the dispersion of which can be simulated using various plume models. Examples of simulated plume scenes are given, one of which contains an existing plume which is replicated using known emission information. Comparison of the real and simulated plume brightness temperatures yielded differences on the order of 0.2 K.
Based on simulated atmospheric and sensor effects, we identify spectral resolution and per-channel signal-to-noise ratio (SNR) requirements for thermal infrared spectrometers that allow effluent quantification to any desired precision. This work is based on the use of MODTRAN-4 to explore the effects of temperature contrast and effluent concentration on the spectral slopes of particular absorption features. These slopes can be estimated from remotely sensed spectral data by use of least-squares techniques. The precision of these estimates is based on two factors related to spectral quality: the number of spectral samples that lie along an absorption feature and the radiometric accuracy of the samples themselves. The least-squares process also calculates the slope estimation error variance, which is related to the effluent quantification uncertainty by the same function that maps the slope itself to effluent quantity. The effluent quantification precision is thus shown to be a function of the spacing between spectral channels and the per-channel SNR. The relationship between SNR, channel spacing and effluent quantification precision is expressed as an equation defining a surface of constant "difficulty." This surface can be used to evaluate parameter sensitivities of sensors in design, to appropriately task sensors, or to evaluate effluent quantification tasks in terms of feasibility.
This paper presents a method of enhancing linear and curvilinear image features, such as those corresponding to tissue discontinuities in medical ultrasound. The method is an extension of a template based technique for line enhancement which produces a test statistic at each point by projecting the pixels near that point onto a line segment, varying the orientation of the segment to maximize the projected value, and retaining the projected value as the test statistic. In the past, we have not made use of information about which angle produced the maximum value at each point. In this paper, we compute a histogram of the angles near each point to gain an indication of the direction of larger scale linear features lying nearby. Mathematically, we wish to estimate a set of prior probabilities for the orientation of line segments that pass through each point. The priors can then be used to improve the power of the Bayesian line detection procedure. In addition, they can also be used to improve the visual quality of the image produced by plotting the test statistics on an image raster. We have found that such an image is revealing because it shows more sharply the edges of the linear components, making them more clearly visible and their fringes more distinguishable from the background. With the incorporation of prior information, the processed image shows a further improvement in visual and machine detectability of linear components, due to increased difference in gray level between points lying on edges and those lying away. This technique has potential to significantly improve the machine detectability of tissue discontinuities in medical ultrasound, as well as linear features in other forms of computed imaging.