The Flat Field technique normalizes the spectrum of each pixel in the image using the average spectrum from a region of flat reflectance within the scene. Some knowledge of the area is useful, although you can search through the data set for flat field prospects. This technique assumes that all of the spectral features in the flat field region are due to the atmosphere and the solar spectrum. You can use an ROI to outline the area known to have flat reflectance and calculate its average spectrum. Then, divide the data set by this flat field average spectrum, effectively removing the shape of the solar spectrum and atmospheric scattering and absorptions.
The Empirical Line technique also requires some detailed knowledge of the scene. In this instance, you collect field or laboratory reflectance spectra for two or more known target areas in the image (usually including a dark and a bright area). Select the targeted regions using ROIs. Calculate a linear regression between the field or lab reflectance spectra and the data radiance spectra for the target areas. This regression line, based upon the bright and dark targets, is used to predict the surface reflectance spectrum for each pixel.
The Internal Average Reflectance (IAR) technique can be used when working in an area for which no ground truth data exists. Calculate the average spectrum of the entire image, then divide this spectrum into each pixel in the image to calculate a relative reflectance.
The Dark Subtraction technique, typically performed on multispectral data, is used to remove the additive effect of scattered light. A dark spectrum is subtracted from every pixel spectrum. The dark spectrum can be defined as the mean spectrum of a dark region in the image, as the minimum value in each band in the image, or as a user-defined spectrum. Commonly a user-defined spectrum, derived by examination of the band histograms for the lowest significant values, is used for the dark spectrum.
ENVI also provides a separate Atmospheric Correction Module (ACM) that includes two routines to convert both multispectral and hyperspectral data to reflectance. FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) is a first-principles modeling approach for applying atmospheric and solar
corrections to data sets. QUAC (Quick Atmospheric Correction) works directly with the observed pixel spectra in a scene, without ancillary information. If a sensor does not have proper radiometric or wavelength calibration, or if the solar illumination intensity is unknown, QUAC can still retrieve reasonably accurate reflectance spectra as long as the scene is diverse and there are enough dark pixels to allow for a good estimation of the baseline spectrum.
Conversion of Landsat TM Data to Reflectance
For Landsat TM Geotiff data, the factors that are used to convert a particular scene to reflectance change through the years as the methodology of producing the data changes. Therefore, the factors that coincide with the processing date, just before delivery to the customer, are the correct ones to use. Old factors should not be used with recently delivered data. Therefore, we will use input from a 2009 meta data file to convert the 1985 TM data (delivered in 2009) to reflectance.
Exercise #1: Preprocess Landsat TM Image
.1. From the ENVI Toolbox, expand Radiometric Correction and double click on Calibrate Landsat. The Landsat Calibration Input File dialog appears. We don’t have the files open yet, so in this dialog click on Open New File.
2. Browse to the ENVI_coursefiles\envidata\veg directory and select
L5033032_03219850812_MTL.txt and click Open. This is the metadata file that contains the factors we will use to convert the data to reflectance. If you get an error message about
Example Application: Vegetation Analysis Preprocessing
see in the dimensions line (Dims) it specifies one “band” (4061 x 3581 x 1). Select the second listing and note that it accesses all six bands in the six separate files (we won’t use band 6, the thermal IR channel). This six band file is actually a kind of meta file – or virtual file – that physically does not exist. Before you click OK, you will select a Spatial Subset.
3. Click on Spatial Subset. In the Samples field, type 711 and hit the ENTER key. Then in the To field, type 1286. When typing in values in ENVI, is it crucial to remember to hit the ENTER key. In some cases, ENVI will not recognize that you typed in anything unless you hit ENTER. Also, hitting ENTER may also move you automatically to the next parameter as it does in this dialog. In the Lines field, type 4302 to 4884. Click OK. And Click OK in the Landsat Calibration Input File dialog.
4. The ENVI Landsat Calibration dialog appears.
5. Given the information from the meta data file about this data set, the parameters should look like this:
Landsat Satellite Sensors: Landsat 5 TM Data Acquisition Month: August
Data Acquisition Day: 12 Data Acquisition Year: 1985 Sun Elevation (deg): 54.2475 Calibration Type: Reflectance
6. Type Bldr_ref as the output filename and click OK. After the calibration is performed, the spatial subset you specified should be shown in the display.
Dark Subtraction Example Application: Vegetation Analysis
7. After the calibration is performed, the spatial subset you specified should be shown in the display. You may also see part of the full scene. If so, right click on it in the Layer Manager and select Remove. Then with only the TM subset in the display, click on the Zoom To Full Extent icon in the menu bar.