Using a Fourier transform infrared field spectrometer, spectral infrared radiance measurements were made of several generated gas plumes against both a uniform sky and terrestrial background. Background temperature, spectral complexity, and physical homogeneity each influenced the success of emissive infrared spectral sensing technology in detecting and identifying the presence of a gas plume and its component constituents. As expected, high temperature contrast and uniform backgrounds provided the best conditions for detectibility and diagnostic identification. This report will summarize some of SITAC's findings concerning plume detectability, including the importance of plume cooling, plumes in emission and absorption, the effects of optical thickness, and the effects of condensing plumes on gas detection.
The ACORN atmospheric correction routine was evaluated using criteria established in a precious performance assessment effort. The data utilized in this analysis represented a variety of background and atmospheric conditions, and were collected by the HYDICE imaging spectrometer. The baseline technique used to match retrieved reflectance spectra with ground truth was the matched filter with the bad bands deleted. Additional investigations were conducted to examine the effects on performance when the spectral angle mapper and the mixture tuned matched filter algorithms were used in place of the matched filter and when different numbers of bands were employed during spectral matching. Results substantiated the conclusions drawn from the previous study that the empirical line method ground truth-based atmospheric correction technique generally out-performs existing model-based techniques, such as ACORN.
Many targets that remote sensing scientists encounter when conducting their research experiments do not lend themselves to laboratory measurement of their surface optical properties. Removal of these targets from the field can change their biotic condition, disturb the surface composition, and change the moisture content of the sample. These parameters, as well as numerous others, have a marked influence on surface optical properties such as spectral and bi-directional emissivity. This necessitates the collection of emissivity spectra in the field. The propagation of numerous devices for the measurement of midwave and longwave emissivity in the field has occurred in recent years. How good are these devices and how does the accuracy of the spectra they produce compare to the tried and true laboratory devices that have been around for decades? A number of temperature/emissivity separation algorithms will be demonstrated on data collected with a field portable Fourier transform infrared (FTIR) spectrometer and the merits and resulting accuracy compared to laboratory spectra made of these identical samples. A brief look at off-nadir view geometries will also be presented to alert scientists to the possible sources of error in these spectra that may result when using sensing systems that do not look straight down on targets or when their nadir looking sensor is looking at a tilted target.
KEYWORDS: LIDAR, Vegetation, Image classification, Data fusion, Data centers, Image resolution, Information technology, Information fusion, Binary data, Spectral resolution
This paper describes a methodology developed at the Spectral Information Technology Applications Center (SITAC) to combine information derived from high resolution LIDAR elevation data with information derived form hyperspectral data to characterize tree canopies. High resolution elevation data are used to detect abrupt changes in elevation, indicative of man-made structures or certain natural features. The underlying elevation is estimated by first masking out the pertinent structures or features and then interpolating. Structure or feature height is then calculated as the difference between the original elevation and the interpolated elevation. This procedure is applied to a high resolution LIDAR elevation data set of an open forest scene to produce a tree height image. These tree height data are then combined with other tree information to infer trunk diameter. Hyperspectral data are employed to detect as well as characterize man-made and natural structures. Fusion of hyperspectral information with elevation information promises benefits to remote sensing applications.
The development of highly portable field devices for measuring midwave and longwave infrared emissivity spectra has greatly enhanced the ability of scientists to develop and verify exploitation algorithms designed to operate in these spectral regions. These data, however, need to be collected properly in order to prove useful once the scientists return from the field. Attention to the removal of environmental factors such as reflected downwelling atmospheric and background radiance from the measured signal are of paramount importance. Proper separation of temperature and spectral emissivity is also a key factor in obtaining spectra of accurate shape and magnitude. A complete description of the physics governing the collection of field spectral emissivity data will be presented along with the assumptions necessary to obtain useful sample signatures. A detailed look at an example field collection device will be presented and the limitations and considerations when using such a device will be scrutinized. Attention will be drawn to the quality that can be expected from field measurements obtained and the limitations in their use that must be endured.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.