BASIC IMAGE QUERY

ENVI allows you to determine useful information about your imagery. Armed with this information you are better able to determine appropriate methods of analysis. Here you will be introduced to some of the more basic query operations.

Display the cursor location: It is often very useful to be able to determine the location and spectral values of selected pixels. This could be useful for identifying training regions, prior to image classification for instance. To display the cursor location and value click Basic Tools on the ENVI main menu. Then select Cursor Location / Value. Alternatively, you can click the right button in the Main window to bring up the Functions Menu. Left click the Functions button then select Interactive Analysis - Cursor Location / Value.

Dialog descring the location of the cursor and the value of the pixel
Cursor / location dialog

A dialogue box will appear displaying the location (column,row) of the cursor in the Main, Scroll or Zoom windows. The dialogue also displays the screen value (pixel values following the default contrast stretching) and the actual data value(s) of the pixel underneath the cross hair cursor. You will learn more about contrast stretching in the next lesson. At the moment just take note that the procedure alters the data values, how and why will be explained later.

Display image profiles: It may be useful to generate profiles (analagous to cross sections) accross the image. These can be used to show how pixel intensity changes vary accross the image. The variations in intensity can be attributed to factors such as cover type (urban, vegetation etc) and topography. ENVI allows you to select X (horizontal), Y (vertical) and user defined plots. As well as investigating changes in intensity spatially, ENVI gives the option of displaying Z (spectral) profiles, which allow you to explore the dimensionality of the image. These plot the data values associated with a single pixel, that is, for a particular point the reflectance response of each band is plotted.

To display any of these image profiles click the right button in the Main window to bring up the Functions Menu. Left click the Functions button then select Profiles and the type of profile you want to view. Position the scroll indicator box over Kowloon centred roughly over the old runway at Kai Tak airport. Now display the X profile plot window. To control the positioning of the profile use the left mouse button to select a position on the image. The profile then moves to intersect the pixel you have selected. Select a pixel near the eastern end of the runway. Your main window and plot should appear similar to that on the right. The red vertical line in the plot window relates to the position of the pixel (alternatively called sample) you selected along the profile. The three ploted lines relate to the intensity of each pixel along the profile for each of the image bands that you have displayed.

It is very easy to see the effect the three main covertypes in the image, have on pixel intensities. You can see that water pixels (immediately left and right of the red vertical line) combine to have a flat profile (little intensity variation) in each band, with progressively lower values as we move from the blue to red image bands (which relates to a shift from short to longer wavelengths). Heavily vegetated pixels are those in which the red band (which relates to the NIR wavelength) is higher than the others. Urban pixels are characterised by generally high variation and reflectance in all bands but with slightly higher intensity for shorter (blue and green bands) than longer (red band) wavelengths.

Main image window with profile line
Main Window showing profile location

 

 

plot window
Plot window

Notice from the profile plot window that all the pixel intensity values are clustered within a narrow range, somewhere between 10 and 70. In the next section you will learn about contast stretching imagery. This is concerned with spreading values from a narrow range into a much larger range, so that visually we can gather more information from the imagery. Sounds complicated? Well its not, just proceed to the next lesson to learn how it is done.


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