introduction to hyperspectral data

10 08 visible near infrared h2o o3 o2 h2o o2 h2o h2o

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: r Infrared H2O O3 O2 H2O O2 H2O H2O CO2 H2O Middle Infrared H2O, O2 CO2 CO2 H2O, CO2 CO2 Transmittance 0.6 0.4 0.2 0 0.5 1.0 1.5 2.0 2.5 Wavelength (micrometers) Plot of atmospheric transmittance versus wavelength for typical atmospheric conditions. Transmittance is the proportion of the incident solar energy that reaches the ground surface. Absorption by the labeled gases causes pronounced lows in the curve, while scattering is responsible for the smooth decrease in transmittance with decreasing wavelength in the near infrared through visible wavelength range. Sensor Effects A sensor converts detected radiance in each wavelength channel to an electric signal which is scaled and quantized into discrete integer values that represent “encoded” radiance values. Variations between detectors within an array, as well as temporal changes in detectors, may require that raw measurements be scaled and/or offset to produce comparable values. page 13 Introduction to Hyperspectral Imaging Reflectance Conversion I In order to directly compare hyperspectral image spectra with reference reflectance spectra, the encoded radiance values in the image must be converted to reflectance. A comprehensive conversion must account for the solar source spectrum, lighting effects due to sun angle and topography, atmospheric transmission, and sensor gain. In mathematical terms, the ground reflectance spectrum is multiplied (on a wavelength per wavelength basis) by these effects to produce the measured radiance spectrum. Two other effects contribute in an additive fashion to the radiance spectrum: sensor offset (internal instrument noise) and path radiance due to atmospheric scattering. Several commonly used reflectance conversion strategies are discussed below and on the following page. Some strategies use only information drawn from the image, while others require varying degrees of knowledge of the surface reflectance properties and the atmospheric conditions at the time the image was acquired. Flat Field Conversion This image-based method requires that the image include a uniform area that has a relatively flat spectral reflectance curve. The mean spectrum of such an area would be dominated by the combined effects of solar irradiance and atmospheric scattering and absorption The scene is converted to “relative” reflectance by dividing each image spectrum by the flat field mean spectrum. The selected flat field should be bright in order to reduce the effects of image noise on the conversion. Since few if any materials in natural landscapes have a completely flat reflectance spectrum, finding a suitable “flat field” is difficult for most scenes. For desert scenes, salt-encrusted dry lake beds present a relatively flat spectrum, and bright man-made materials such as concrete may serve in urban scenes. Any significant spectral absorption features in the flat field spectrum will give rise to spurious features in the calculated relative reflectance spectra. If there is significant elevation variation within the scene, the converted spectra will also incorporate residual effects of topographic shading and atmospheric path differences. Average Relative Reflectance Conversion This method also normalizes image spectra by dividing by a mean spectrum, but derives the mean spectrum from the entire image. Before computing the mean spectrum, the radiance values in each image spectrum are scaled so that their sum is constant over the entire image. This adjustment largely removes topographic shading and other overall brightness variations. The method assumes that the scene is heterogeneous enough that spatial variations in spectral reflectance characteristics will cancel out, producing a mean spectrum similar to the flat field spectrum described above. This assumption is not true of all scenes, and when it is not true the method will produce relative reflectance spectra that contain spurious spectral features. page 14 Introduction to Hyperspectral Imaging Reflectance Conversion II The image-based conversion methods discussed on the previous page only account for multiplicative contributions to the image spectra. Most studies that have used these methods have focused on mapping minerals using shortwave infrared spectra (2.0 to 2.5 µm) for which the additive effect of atmospheric path radiance is minimal. If the spectra to be analyzed include the visible and near infrared ranges, however, path radiance effects should not be neglected. If the scene includes dark materials or deep topographic shadows, an approximate correction can be made by determining (for each band) the minimum brightness value (or the average value of a shadowed area) and subtracting it from each pixel in the band. Empirical Line Method Field researchers using hyperspectral imagery typically use field reflectance measurements from the image area to convert the image data to reflectance. Field reflectance spectra must be acquired from two or more uniform ground target areas. Target areas...
View Full Document

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.

Ask a homework question - tutors are online