We describe an in-scene technique for accurate wavelength calibration of our airborne MAHI (Mid-infrared Airborne Hyperspectral Imager) sensor using modelled atmospheric spectral features. MAHI operates in the 3.3 to 5.4 μm region with a spectral sampling of 3.3 nm. The new technique significantly improves the accuracy over a lab technique using plastic film spectral features. We demonstrate the technique’s performance against: poorly known spectral response function; error in the initial wavelength grid guess; and deviation of the measured pixel spectra from the reference atmospheric spectrum. Performance of the new calibration technique for a recent airborne campaign in the Los Angeles area is demonstrated.
We describe a new algorithm, QUAC-IR (QUick Atmospheric Correction in the InfraRed), for automated, fast, atmospheric correction of LWIR (Long Wavelength InfraRed) hyperspectral imagery (HSI) and multi-spectral imagery (MSI) in the ~7-14 mm spectral region. QUAC-IR is an in-scene based algorithm, similar to the widely used ISAC (In- Scene Atmospheric Correction) algorithm. It improves upon the ISAC approach in several key ways, including providing absolute, versus relative, sensor-to-ground transmittances and radiances, as well as an estimate of the atmospheric downwelling sky radiance. The latter is important for retrieving emissivity from a reflective (i.e., non-blackbody) pixel. The key aspect of QUAC-IR is that it explicitly searches for blackbody pixels using an efficient approach involving a small number of spectral channels in which the atmospheric radiative transfer is dominated by the water continuum. This allows for fast and simplified Beer's Law (i.e., exponential) scaling of the path transmittance and radiance based on a compact library of pre-computed reference values. We apply QUAC-IR to well-calibrated data from the SEABASS1 and MAKO2 HSI sensors. The results are compared to those from a first-principles physics-based atmospheric code, FLAASH-IR.
A Line-By-Line (LBL) option is being developed for MODTRAN6. The motivation for this development is two-fold. Firstly, when MODTRAN is validated against an independent LBL model, it is difficult to isolate the source of discrepancies. One must verify consistency between pressure, temperature and density profiles, between column density calculations, between continuum and particulate data, between spectral convolution methods, and more. Introducing a LBL option directly within MODTRAN will insure common elements for all calculations other than those used to compute molecular transmittances. The second motivation for the LBL upgrade is that it will enable users to compute high spectral resolution transmittances and radiances for the full range of current MODTRAN applications. In particular, introducing the LBL feature into MODTRAN will enable first-principle calculations of scattered radiances, an option that is often not readily available with LBL models. MODTRAN will compute LBL transmittances within one 0.1 cm-1 spectral bin at a time, marching through the full requested band pass. The LBL algorithm will use the highly accurate, pressure- and temperature-dependent MODTRAN Padé approximant fits of the contribution from line tails to define the absorption from all molecular transitions centered more than 0.05 cm-1 from each 0.1 cm-1 spectral bin. The beauty of this approach is that the on-the-fly computations for each 0.1 cm-1 bin will only require explicit LBL summing of transitions centered within a 0.2 cm-1 spectral region. That is, the contribution from the more distant lines will be pre-computed via the Padé approximants. The status of the LBL effort will be presented. This will include initial thermal and solar radiance calculations, validation calculations, and self-validations of the MODTRAN band model against its own LBL calculations.
Processing long-wave infrared (LWIR) hyperspectral imagery to surface emissivity or reflectance units via atmospheric
compensation and temperature-emissivity separation (TES) affords the opportunity to remotely classify and identify
solid materials with minimal interference from atmospheric effects. This paper describes an automated atmospheric
compensation and TES method, called FLAASH®-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypecubes--
Infrared), and its application to ground-to-ground imagery taken with the Telops Inc. Hyper-Cam interferometric
hyperspectral imager. The results demonstrate that clean, quantitative surface spectra can be obtained, even with highly
reflective (low emissivity) objects such as bare metal and in the presence of some illumination from the surroundings. In
particular, the atmospheric compensation process suppresses the spectral features due to atmospheric water vapor and
ozone, which are especially prominent in reflected sky radiance.
The MODTRAN6 radiative transfer (RT) code is a major advancement over earlier versions of the MODTRAN
atmospheric transmittance and radiance model. This version of the code incorporates modern software ar-
chitecture including an application programming interface, enhanced physics features including a line-by-line
algorithm, a supplementary physics toolkit, and new documentation. The application programming interface
has been developed for ease of integration into user applications. The MODTRAN code has been restructured
towards a modular, object-oriented architecture to simplify upgrades as well as facilitate integration with other
developers' codes. MODTRAN now includes a line-by-line algorithm for high resolution RT calculations as well
as coupling to optical scattering codes for easy implementation of custom aerosols and clouds.
Striping effects, i.e., artifacts that vary systematically with the image column or row, may arise in hyperspectral or multispectral imagery from a variety of sources. One potential source of striping is a physical effect inherent in the measurement, such as a variation in viewing geometry or illumination across the image. More common sources are instrumental artifacts, such as a variation in spectral resolution, wavelength calibration or radiometric calibration, which can result from imperfect corrections for spectral “smile” or detector array nonuniformity. This paper describes a general method of suppressing striping effects in spectral imagery by referencing the image to a spectrally lowdimensional model. The destriping transform for a given column or row is taken to be affine, i.e., specified by a gain and offset. The image cube model is derived from a subset of spectral bands or principal components thereof. The general approach is effective for all types of striping, including broad or narrow, sharp or graduated, and is applicable to radiance data at all optical wavelengths and to reflectance data in the solar (visible through short-wave infrared) wavelength region. Some specific implementations are described, including a method for suppressing effects of viewing angle variation in VNIR-SWIR imagery.
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