Digital filtering implementations for the detection of broad spectral features by direct analysis of passive Fourier transform infrared interferograms

Finite impulse response (FIR) filters and finite impulse response matrix (FIRM) filters are evaluated for use in the detection of volatile organic compounds with wide spectral bands by direct analysis of interferogram data obtained from passive Fourier transform infrared (FT-IR) measurements. Short segments of filtered interferogram points are classified by support vector machines (SVMs) to implement the automated detection of heated plumes of the target analyte, ethanol. The interferograms employed in this study were acquired with a downward-looking passive FT-IR spectrometer mounted on a fixed-wing aircraft. Classifiers are trained with data collected on the ground and subsequently used for the airborne detection. The success of the automated detection depends on the effective removal of background contributions from the interferogram segments. Removing the background signature is complicated when the analyte spectral bands are broad because there is significant overlap between the interferogram representations of the analyte and background. Methods to implement the FIR and FIRM filters while excluding background contributions are explored in this work. When properly optimized, both filtering procedures provide satisfactory classification results for the airborne data. Missed detection rates of 8% or smaller for ethanol and false positive rates of at most 0.8% are realized. The optimization of filter design parameters, the starting interferogram point for filtering, and the length of the interferogram segments used in the pattern recognition is discussed.     

Robust classifier for the automated detection of ammonia in heated plumes by passive fourier transform infrared spectrometry

An automated classification algorithm is implemented for the detection of ammonia vapor in heated plumes by passive Fourier transform infrared (FT-IR) spectrometry. This classification methodology allows the real-time detection of chemical signatures in gaseous effluents such as those generated from industrial processes. The characteristics of real-time implementation and excellent robustness are achieved by an analysis strategy based on the application of band-pass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer, followed by the use of piecewise linear discriminant analysis to obtain a yes/no classification regarding the presence of the analyte signature in the filtered data. The optimal classifier developed through this work is based on only 110 interferogram points and employs a single band-pass filter centered at 945 cm(-)(1) with a pass-band full width at half-maximum of 93 cm(-)(1). The average stop-band attenuation of the optimal filter is 42.1 dB. The robustness of the algorithm is tested by exposing it to chemical releases of sulfur hexafluoride, ethanol, methanol, sulfur dioxide, and hydrogen chloride that were not included in the development of the classifier. Excellent classification performance is demonstrated, with missed ammonia detections occurring at a rate of approximately 1%. The occurrence of false detections is less than 0.1% for SF(6) and less than 0.02% for the other interferences tested.     

Simulated radiance profiles for automating the interpretation of airborne passive multi-spectral infrared images

Methodology is developed for simulating the radiance profiles acquired from airborne passive multispectral infrared imaging measurements of ground sources of volatile organic compounds (VOCs). The simulation model allows the superposition of pure-component laboratory spectra of VOCs onto spectral backgrounds that simulate those acquired during field measurements conducted with a downward-looking infrared line scanner mounted on an aircraft flying at an altitude of 2000-3000 ft (approximately 600-900 m). Wavelength selectivity in the line scanner is accomplished through the use of a multichannel Hg:Cd:Te detector with up to 16 integrated optical filters. These filters allow the detection of absorption and emission signatures of VOCs superimposed on the upwelling infrared background radiance within the instrumental field of view (FOV). By combining simulated radiance profiles containing analyte signatures with field-collected background signatures, supervised pattern recognition methods can be employed to train automated classifiers for use in detecting the signatures of VOCs during field measurements. The targeted application for this methodology is the use of the imaging system to detect releases of VOCs during emergency response scenarios. In the work described here, the simulation model is combined with piecewise linear discriminant analysis to build automated classifiers for detecting ethanol and methanol. Field data collected during controlled releases of ethanol, as well as during a methanol release from an industrial facility, are used to evaluate the methodology.     

Remote detection of volatile organic compounds by passive multispectral infrared imaging measurements

Automated pattern recognition methodology is described for the detection of signatures of volatile organic compounds from passive multispectral infrared imaging data collected from an aircraft platform. Data are acquired in an across-track scanning mode with a downward-looking line scanner based on 8 to 16 spectral channels in the 8-14 and 3-5 microm spectral ranges. Two controlled release experiments are performed in which plumes of ethanol are generated and detected from aircraft overflights at altitudes of 2200 to 2800 ft (671 to 853 m). In addition, a methanol release from a chemical manufacturing facility is monitored. Automated classifiers are developed by application of piecewise linear discriminant analysis to the calibrated, registered, and preprocessed radiance data acquired by the line scanner. Preprocessing steps evaluated include contrast enhancement, temperature-emissivity separation, feature selection, and feature extraction/noise reduction by the minimum noise fraction (MNF) transform. Successful classifiers are developed for both compounds and are tested with data not used in the classifier development. Separation of temperature and emissivity by use of the alpha residual calculation is found to reduce false positive detections to a negligible level, and the MNF transform is shown to enhance detection sensitivity.     

Tunable Fabry-Perot etalon-based long-wavelength infrared imaging spectroradiometer

Imaging spectrometry enables passive, stand-off detection and analysis of the chemical composition of gas plumes and surfaces over wide geographic areas. We describe the use of a long-wavelength infrared imaging spectroradiometer, comprised of a low-order tunable Fabry-Perot etalon coupled to a HgCdTe detector array, to perform multispectral detection of chemical vapor plumes. The tunable Fabry-Perot etalon used in this research provides coverage of the 9.5-14-mum spectral region with a resolution of 7-9 cm(-1). The etalon-based imaging system provides the opportunity to image a scene at only those wavelengths needed for chemical species identification and quantification and thereby minimize the data volume necessary for selective species detection. We present initial results using a brassboard imaging system for stand-off detection and quantification of chemical vapor plumes against near-ambient-temperature backgrounds. These data show detection limits of 22 parts per million by volume times meter (ppmv x m) and 0.6 ppmv x m for dimethyl methyphosphonate and SF(6), respectively, for a gas/background DT of 6 K. The system noise-equivalent spectral radiance is approximately 2 muW cm(-2) sr(-1) mum(-1). Model calculations are presented comparing the measured sensitivity of the sensor to the anticipated signal levels for two chemical release scenarios.     

Dual-aureole and sun spectrometer system for airborne measurements of aerosol optical properties

We have designed an airborne spectrometer system for the simultaneous measurement of the direct sun irradiance and the aureole radiance in two different solid angles. The high-resolution spectral radiation measurements are used to derive vertical profiles of aerosol optical properties. Combined measurements in two solid angles provide better information about the aerosol type without additional and elaborate measuring geometries. It is even possible to discriminate between absorbing and nonabsorbing aerosol types. Furthermore, they allow to apply additional calibration methods and simplify the detection of contaminated data (e.g., by thin cirrus clouds). For the characterization of the detected aerosol type a new index is introduced that is the slope of the aerosol phase function in the forward scattering region. The instrumentation is a flexible modular setup, which has already been successfully applied in airborne and ground-based field campaigns. We describe the setup as well as the calibration of the instrument. In addition, example vertical profiles of aerosol optical properties--including the aureole measurements--are shown and discussed.     

Calibration transfer algorithm for automated qualitative analysis by passive Fourier transform infrared spectrometry

The automated qualitative analysis of passive Fourier transform infrared (FT-IR) remote sensing data is made difficult by the presence in the data of background and instrument-specific variation. For data collected with a single instrument, variation in the data arises from changes in the infrared background radiance, changes in the atmospheric composition within the field-of-view of the spectrometer, and changes in the instrument response function arising from temperature variation in the spectrometer. When more than one spectrometer is used, the variation in detector responses and phase signatures between instruments serves to complicate further the task of implementing an automated processing algorithm for detecting the signature of a target compound. In this work, a combination of signal processing and pattern recognition methodology is applied directly to the interferogram data collected by the FT-IR spectrometer to implement an automated compound detection procedure that is independent of background and instrument-specific variation. The key to this algorithm is the use of highly attenuating digital filters to isolate in the interferogram the frequencies associated with an analyte absorption or emission band while suppressing information at other frequencies. For the test compounds, acetone and sulfur hexafluoride, it is demonstrated that when this digital filtering procedure is coupled with either piecewise linear discriminant analysis or a back-propagation neural network, an automated detection algorithm can be developed with data from a primary instrument and then subsequently used to predict the presence of analyte signatures in data collected with a secondary spectrometer. Correct classification rates in excess of 92% are obtained for both compounds when the algorithm is applied to data collected with the secondary instrument.     

Oceanic radiance model development and validation: application of airborne active-passive ocean color spectral measurements

It is shown that airborne active-passive (laser-solar) ocean color data can be used to develop and validate oceanic radiance models. The two principal inputs to the oceanic radiance model, chlorophyll pigment and incident solar irradiance, are obtained from a nadir-viewing laser-induced fluorescence spectrometer and a zenith-viewing radiometer, respectively. The computed water-leaving radiances are validated by comparison with the calibrated output of a separate nadir-viewing radiometer subsystem. In the North Atlantic Ocean, the calculated and the observed airborne radiances are found to compare very favorably for the 443-, 520-, and 550-nm wavelengths over an ～ 170-km flight track east of St. John's, Newfoundland. The results further suggest that the semianalytical radiance model of ocean color, the airborne active (laser) fluorescence spectrometer, and the passive (solar) radiometric instrumentation are all remarkably precise.     

Far-infrared sensor for cirrus (FIRSC): an aircraft-based Fourier-transform spectrometer to measure cloud radiance

We describe an aircraft-based Fourier-transform spectrometer (FTS) designed to measure the Earth outgoing radiance spectrum in the far-infrared-submillimeter spectral range. The instrument features include a rapid-scan FTS to obtain high spatial resolution from a moving aircraft platform, a sensitive two-channel detector, and a CCD camera for recording the nadir cloud scene with each scan record. Such measurements demonstrate the sensitivity of Earth radiance to high clouds and provide spectral data for improving techniques for remote sensing and retrieval of atmospheric and cloud properties.     

Retrieval of atmospheric-temperature and water-vapor profiles by use of combined satellite and ground-based infrared spectral-radiance measurements

A nonlinear sounding retrieval algorithm is used to produce vertical-temperature and water-vapor profiles from coincident observations taken by the airborne High-resolution Interferometer Sounder (HIS) and the ground-based Atmospheric Emitted Radiance Interferometer (AERI) during the SUbsonic Contrails and Clouds Effects Special Study (SUCCESS). Also, clear sky Geostationary Operational Environmental Satellite (GOES) and AERI radiance measurements, achieved on a daily real-time basis at the Department of Energy's Oklahoma CART (Cloud and Radiation Testbed) site, are used to demonstrate the current profiling capability by use of simultaneous geostationary satellite and ground-based remote sensing observations under clear-sky conditions. The discrepancy principle, a method to find the proper smoothing parameters from the minimum value between the normalized spectral residual norm and the a priori upper bound, is used to demonstrate the feasibility and effectiveness of on-line simultaneous tuning of the multiple weighting and smoothing parameters from the combined satellite/airborne and ground-based measurements for the temperature and water-vapor retrieval in this nonlinear-retrieval process. An objective method to determine the degrees of freedom (d.f.) of the observation signal is derived. The d.f. of the radiance signal for the combined GOES and AERI measurements is larger than that for either instrument alone; while the d.f. of the observation signal for the combined GOES and AERI measurements is larger than that for either instrument alone and of the combined GOES and AERI measurements. The use of simultaneous clear-sky AERI and GOES data now provides improved vertical temperature and moisture soundings on an hourly basis for use in the Atmospheric Radiation Measurement program [J. Appl. Meteorol. 37, 875 (1998)].     

基于支持向量机的印品缺陷分类方法

目的研究印品图像的各类形状缺陷,建立基于支持向量机(Support vector machine,SVM)的印品形状缺陷分类模型。方法对印品进行符合人眼视觉特性的缺陷识别,并对提取缺陷进行特征分析。将特征数据导入支持向量机进行训练学习,SVM分类器对缺陷图像进行测试。结果分类器对点缺陷和面缺陷的识别率为100%,对线缺陷的分类准确率达93.94%。结论基于SVM的缺陷分类方法能较好地满足印品质量检测的需求。     

Airborne CO(2) coherent lidar for measurements of atmospheric aerosol and cloud backscatter

An airborne CO(2) coherent lidar has been developed and flown on over 30 flights of the NASA DC-8 research aircraft to obtain aerosol and cloud backscatter and extinction data at a wavelength near 9μm. Designed to operate in either zenith- or nadir-directed modes, the lidar can be used to measure vertical profiles of backscatter throughout the vertical extent of the troposphere and the lower stratosphere. Backscatter measurements in absolute units are obtained through a hard-target calibration methodology. The use of coherent detection results in high sensitivity and narrow field of view, the latter property greatly reducing multiple-scattering effects. Aerosol backscatter profile intercomparisons with other airborne and ground-based CO(2) lidars were conducted during instrument checkout flights over the NASA Ames Research Center before extended depolyment over the Pacific Ocean. Selected results from data taken during the flights over the Pacific Ocean are presented, emphasizing intercom arisons with backscatter profile data obtained at 1.06 μm with a NASA Goddard Space Flight Center Nd:YAG lidar on the same flights.     

Mapping tropospheric ozone profiles from an airborne ultraviolet-visible spectrometer

We present a novel technique for retrieving ozone (O3) profiles and especially tropospheric O3 from airborne UV/visible spectrometer measurements. This technique utilizes radiance spectra from one down-looking and two up-looking (85 degrees and 75 degrees) directions, taking advantage of the O3 absorption structure in the Huggins (300-340-nm) and Chappuis (530-650-nm) bands. This technique is especially sensitive to tropospheric O3 below and < or =8 km above the aircraft with a vertical resolution of 2-6 km and is sensitive to lower and middle stratospheric O3 with a vertical resolution of 8-15 km. It can measure tropospheric O3 at spatial resolutions of 2 km x 2 km or higher and is therefore well suited for regional air-quality studies and validation of satellite measurements.     

Reflectance recovery for airborne sensor images of 3D scenes

An airborne sensor measures the radiance spectrum, which is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present an algorithm to estimate the surface spectral reflectance, given the sensor radiance spectrum corresponding to a single pixel. The algorithm uses a nonlinear physics-based image formation model. A low-dimensional linear subspace model is used for the reflectance spectra. The solar radiance, sky radiance, and path-scattered radiance are dependent on the environmental conditions and viewing geometry, and this interdependence is considered by using a coupled-subspace model for these spectra. The algorithm uses the Levenberg-Marquardt method to estimate the subspace model parameters. We have applied the algorithm to a large set of synthetic and real data.     

Validation of fascod3 and modtran3: comparison of model calculations with ground-based and airborne interferometer observations under clear-sky conditions

The validation of fascod3 and modtran3 against ground-based and airborne high-resolution Michelson interferometer measurements under clear-sky conditions is presented. Important considerations including water vapor continuum, frequency-dependent sea surface emissivity in the IR window region, and spectral resolution of modtran3 in the comparison of model calculations with high-resolution interferometer measurements are discussed. Our results indicate that it is not adequate to assume sea surface emissivity of 1.0 [?(ν) = 1.0] or a constant in the simulation of upwelling radiance observed by the airborne Michelson interferometer. The use of spectral emissivity (frequency-dependent emissivity) leads to much better agreement between model calculations and interferometer measurements in the IR window region from 750.0 to 1050.0 cm(-1). This could have important implications for the retrieval of sea surface temperature, thin cirrus properties, and aerosol parameters because of the sea surface emissivity of 1.0 assumption commonly used by many researchers. Comparisons of modtran3 calculations with interferometer measurements show that the agreement might not be adequate at the nominal resolution of 2.0 cm(-1), and further spectral degradation might be necessary to improve the agreement between measurements and modtran3 calculations. modtran should be used with caution for relatively high spectral resolution remote-sensing applications.     

Modulated FT-Raman Fiber-Optic Spectroscopy: A Technique for Remotely Monitoring High-Temperature Reactions in Real-Time

A modification to a commercial FT-Raman spectrometer is presented for the elimination of thermal backgrounds in FT-Raman spectra. The modification involves the use of a mechanical chopper to modulate the CW laser, remote collection of the signal via fiber optics, and connection of a dual-phase digital signal processor lock-in amplifier between the detector and the spectrometer's collection electronics to demodulate and filter the optical signals. The resulting modulated FT-Raman fiber-optic spectrometer is capable of completely eliminating thermal backgrounds at temperatures exceeding 370 °C. In addition, the signal/noise of generated Raman spectra is greater than for spectra collected with the conventional FT-Raman under identical conditions and incident laser power. This is true for both room-temperature and hot samples. The method allows collection of data using preexisting spectrometer software. The total cost of the modification (excluding fiber optics) is ～$3000 and requires less than 2 h to implement. This is the first report of FT-Raman spectra collected at temperatures in excess of 300 °C in the absence of thermal backgrounds.     

Performance analysis of EM,SVD, and SVM classifiers in classification of carcinogenic regions of medical images

In this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.     

Error analysis for estimation of trace vapor concentration pathlength in stack plumes

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.