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.

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered.

To demonstrate the utility of EO-1 data, combined analysis of panchromatic, multispectral (ALI, Advanced Land Imager) and hyperspectral (Hyperion) data was conducted. In particular, the value added by HSI with additional spectral information will be illustrated. Data sets from Coleambally Irrigation Area, Australia on 7 March 2000 and San Francisco Bay area on 17 January 2000 are employed for the analysis. Analysis examples are shown for surface characterization, anomaly detection, spectral unmixing and image sharpening.

A cloud cover detection algorithm was developed for application to EO-1 Hyperion hyperspectral data. The algorithm uses only band in the reflected solar spectral regions to discriminate clouds from surface features such as snow, ice, and desert sand and was designed to be used on-board the EO-1 satellite as part of the EO-1 Extended Mission Phase of the EO-1 Science Program. The code was developed using 20 Hyperion scenes with varying cloud amount, cloud type, underlying surface characteristics and seasonal conditions. Results from the application of the algorithm to these test scenes are given with a discussion on the accuracy of the procedure used in the cloud cover discrimination.

The EO-1 satellite consist of three imaging sensors including Hyperion, which is currently the only space-borne HSI data source. The unique capability of hyperspectral sensing for coastal characterization provides the possibility for exploring the coupled effects of coastal features and a more accurate atmospheric compensation. It is illustrated in this paper that hyperspectral data inherently provide more information for feature extraction than multispectral data despite Hyperion having a relatively low SNR. Chlorophyll retrievals are obtained from Hyperion data and the results compare favorably with data from other sources. The analysis illustrates the potential value of Hyperion (and HSI in general) data to coastal characterization.

Spectral data provide opportunities to discriminate targets from background clutter and to detect partially concealed objects. However, data with high spatial resolution generally are not synoptic scale (hundreds of kilometers). By analyzing courser resolution synoptic imagery, high-resolution sensors can then be cued to areas of potential targets. Multispectral images (Landsat 7) are combined with ancillary data-lines of communication, digital elevation models (DEMS), etc.-in order to characterize the scene of interest. Two scenes were examined: regions of the Balkans (Kosovo, Bosnia-Herzegovina, Montenegro, and Serbia), and Iraq. Three data products result from this fusion of data sources: (1) land cover classification, (2) trafficability analysis, and (3) "hide area" delineation. In addition, the fusion of imagery with elevation models provides a beneficial perspective to the analyst. The classification is a thematic map of the different land cover types produced through a combination of supervised and unsupervised means. Trafficability may be dependent on a number of factors including land cover type, vegetation density, soil moisture content, and access to major roads or navigable rivers. In addition to land cover-based trafficability analysis, terrain-based trafficability uses DEM-derived slope information to determine vehicle accessibility. Determination of "hide areas" may be another important product. These can be defined by their proximity to the forest perimeter, access to roads, and the underlying terrain. As a result of these analyses carried out on the synoptic scale, the collective size of the areas of interest provided to the next sensor in the intelligence chain may be greatly reduced.

A conventional approach to HSI processing and exploitation has been to first perform atmospheric compensation so that surface features can be properly characterized. In this paper, the application of visible and IR spectral information to atmospheric characterization is discussed and illustrated with hyperspectral data in the VNIR, SWIR and MWIR data.AVIRIS and ARES data are utilized. The Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) sensor contains 224 bands, each with a spectral bandwidth of approximately 10 mn, allowing it to cover the entire range between 4 and 2.5 mum. For a NASA ER-2 flight altitude of 20 km, each pixel is 20 m in size, yielding a ground swath width of approximately 10 km. The Airborne Remote Earth Sensing (ARES) sensor was flown on a NASA WB-57 aircraft operated from approximately 15 km altitude. Spectral radiance data from 2.0 to 6.0 mum in 75 contiguous bands were collected. Pixel resolution is approximately 17 by 4.5 m(2) with a swath width of 800 m.Examples of data applications include atmospheric water vapor retrieval, aerosol characterization, delineation of natural and manmade clouds/plumes, and cloud depiction. It is illustrated that though each application may only require a few spectral bands, the ultimate strength of HSI exploitation lies in the simultaneous and adaptive retrievals of atmospheric and surface features. Inter-relationships among different bands are also demonstrated and these are the physical basis for the optimal exploitation of spectral information.

Longwave Infrared (LWIR) radiation comprising atmospheric and surface emissions provides information for a number of applications including atmospheric profiling, surface temperature and emissivity estimation, and cloud depiction and characterization. The LWIR spectrum also contains absorption lines for numerous molecular species which can be utilized in quantifying species amounts. Modeling the absorption and emission from gaseous species using various radiative transfer codes such as MODTRAN-4 and FASE (a follow-on to the line-by-line radiative transfer code FASCODE) provides insight into the radiative signature of these elements as viewed from an airborne or space-borne platform and provides a basis for analysis of LWIR hyperspectral measurements. In this study, a model platform was developed for the investigation of the passive outgoing radiance from a scene containing an effluent plume layer. The effects of various scene and model parameters including ambient and plume temperatures, plume concentration, as well as the surface temperature and emissivity on the outgoing radiance were estimated. A simple equation relating the various components of the outgoing radiance was used to study the scale of the component contributions. A number of examples were given depicting the spectral radiance from plumes composed of single or multiple effluent gases as would be observed by typical airborne sensors. The issue of detectability and spectral identification was also discussed.

Two approaches, one for discriminating features in a set of AVIRIS scenes dominated by areas of smoke, plumes, clouds and burning grassland as well as scarred (burned) areas and another for identifying those features are presented here. A semiautomated feature extraction approach using principal components analysis was used to separate the scenes into feature classes. Typically, only 3 component images were used to classify the image. A physics-based approach which utilized the spectral diversity of the features in the image was used to identify the nature of the classes produced in the component analysis. The results from this study show how the two approaches can be used in unison to fully characterize a smoke or cloud-filled scene.

This paper describes work that has been done to assess the performance of a three-dimensional velocity filtering algorithm for detecting and tracking a moving target against a cluttered background. The algorithm is considerably more sensitive than conventional frame-differencing techniques, and can detect fainter targets, at the cost of some additional computational complexity. Test images have been generated which contain a target in a cloudscene background, and incorporate typical sensor effects such as noise, blurring (due to both jitter and optical point spread function) and drift. Results are shown in terms of false-alarm rate as a function of sensor noise for different targets, conditions, and wavebands. Some of the trade-offs that have been explored include the choice of either a narrow absorption band (MWIR, 4.22-4.45 mu) for reduced clutter, or a wider band (MLWIR, 5.4 - 7.2 mu) for increased signal. The optimum band is shown to depend on the sensor noise characteristics and the platform stability.

Background phenomenology databases and models are essential for the design and assessment of electro-optical sensing systems. The MWIR band has been proposed to satisfy a number of specific requirements in the DoD space based mission areas. However, the phenomenology database in the MWIR to support the design and performance evaluation is limited. Currently the high resolution infrared radiation sounder (HIRS/2) onboard NOAA 12, an operational polar orbiting environmental and weather satellite, offers continual global coverage of several bands in the MWIR. In particular, Channel 17 operates in the heart of the 4.23 micrometer carbon-dioxide band. Though with coarse resolution (approximately 20 km), the vast database offers a good baseline understanding of the MWIR phenomenology related to space based MWIR systems on (1) amplitude variation as function of latitude, season, and solar angle, (2) correlation to relevant MWIR features such as high-altitude clouds, stratospheric warming, aurora and other geomagnetic activities, (3) identification of potential low spatial frequency atmospheric features, and (4) comparison with future dedicated measurements. Statistical analysis on selected multiple orbits over all seasons and geographical regions was conducted. Global magnitude and variation in these bands were established. The overall spatial gradient on the 50 km scale was shown to be within sensor noise; this established the upper bound of spatial frequency in the heart-of-the-carbon-dioxide-band. Results also compared favorably with predictions from atmospheric background models such as the Synthetic High Altitude Radiance Code (SHARC-3).

Water outgassing at altitudes greater than 250 km was observed during the Lincoln Laboratory HAVE SLED II series of sounding rocket experiments. The major spectral features include the water nu2 band at 6.3 mum and rotational emission at wavelengths greater than 15 mum. These data contain information on the rotational and nu2 vibrational temperatures, as well as the water outgassing rate. An analytical model has been developed to predict the long wavelength infrared spectral radiance from the outgassing of water from a vehicle surface. An outgassing rate of 0.3-3 g/s of H2O was determined to be consistent with the data. The H2O rotational and vibrational temperatures were estimated to be 220 +/- 30 K and 265 +/- 30 K, respectively.