introduction to hyperspectral data

Target areas should have widely different brightness

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Unformatted text preview: should have widely different brightness and be large enough to recognize in the image. Using the image radiance and ground reflectance values for the target areas, a linear equation relating radiance to reflectance can be derived for each image band. Bright target In a plot of radiance versus reflectance, the slope of the calculated line quantifies the combined effects Slope = gain of the multiplicative radiance factors (gain), while the intercept with the radiance axis represents the additive component (offset). These values are then Dark target used to convert each image band to apparent reflecIntercept = offset tance. The final values should be considered “apparent” reflectance because the conversion does Reflectance not account for possible effects of topography Reflectance conversion within the scene (shading and atmospheric path parameters for a single length differences). image band using known Modeling Methods Radiative-transfer computer models start with a simulated solar irradiance spectrum, then compute the scene radiance effects of solar elevation (derived from the day and time of the scene) and atmospheric scattering and absorption. In the absence of measurements of actual atmospheric conditions, the user must estimate some input parameters, such as amount and distribution of scattering agents. Absorption by well-mixed gases (CO2 and O2) is assumed to be uniform across a scene but absorption due to water vapor is often variable. Water vapor absorption effects can be estimated and corrected individually for each image pixel using portions of the spectra that include water absorption bands. The final apparent reflectance values may still incorporate the effects of topographic shading, however. page 15 Radiance target reflectance values. Introduction to Hyperspectral Imaging Strategies for Image Analysis The table below lists some of the imaging spectrometers currently being operated for research or commercial purposes. The hyperspectral images produced by these sensors present a challenge for the analyst. They provide the fine spectral resolution needed to characterize the spectral properties of surface materials but the volume of data in a single scene can seem overwhelming. The difference in spectral information between two adjacent wavelength bands is typically very small and their grayscale images therefore appear nearly identical. Much of the data in a scene therefore would seem to be redundant, but embedded in it is critical information that often can be used to identify the ground surface materials. Finding appropriate tools and approaches for visualizing and analyzing the essential information in a hyperspectral scene remains an area of active research. Most approaches to analyzing hyperspectral images concentrate on the spectral information in individual image cells, rather than spatial variations within individual bands or groups of bands. The statistical classification (clustering) methods often used with multispectral images can also be applied to hyperspectral images but may need to be adapted to handle their high dimensionality (Landgrebe, in press). More sophisticated methods combine both spectral and spatial analysis. The following pages detail some of the popular methods of analyzing the spectral content of hyperspectral images. A Sample of Research and Commercial Imaging Spectrometers Sensor Or g a n iz a tio n C o u n try U ni t e d S ta te s F i nl a nd C a na d a U ni t e d S ta te s N u m b e r Wa v e l e n g t h o f B a n d s R a n g e (µ m ) 224 286 288 2 11 128 128 0 .4 - 2 .5 0 .4 5 - 0 .9 0 .4 3 - 0 .8 7 0 .4 - 1 2 .0 0 .4 - 2 .4 5 0 .4 - 2 .4 5 AV IRIS A IS A CASI D A IS 2 11 5 HYM A P P ROB E - 1 NA S A S p e c t r a l Im a g i ng L t d . It r e s Re s e a r c h GE R C o rp . Int e g r a t e d S p e c t r o ni c s A us t r a l i a P ty L td E a r t h S e a r c h S c i e nc e s Inc . U ni t e d S ta te s page 16 Introduction to Hyperspectral Imaging Match Each Image Spectrum One approach to analyzing a hyperspectral image is to attempt to match each image spectrum individually to one of the reference reflectance spectra in a spectral library. This approach requires an accurate conversion of image spectra to reflectance. It works best if the scene includes extensive areas of essentially pure materials that have corresponding reflectance spectra in the reference library. An observed spectrum will typically show varying degrees of match to a number of similar reference spectra. The matching reference spectra must be ranked using some measure of goodness of fit, with the best match designated the “winner.” Spectral matching is compli1.0 Image cated by the fact that most 0.8 hyperspectral scenes include many image pixels that repre0.6 Library sent spatial mixtures of different 0.4 materials (see page 10). The resulting composite image 0.2 spectra may match a variety of 2.1 2.2 2.3 2.4 “pure” reference spectra to Wavelength (micrometers) varying degrees, perhaps inSample image spectrum and a matched spectrum of the mineral alunite from the USGS Spectral cluding some spectra of Library (goodness of fit = 0.91)...
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This note was uploaded on 12/16/2010 for the course ENV 148 taught by Professor Chang during the Spring '10 term at APU Japan.

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