Eyes in the Sky II > GIT Web Course > Module 3 > Week 11 > Intro to Color Imaging

Week 11: Using Satellite Data to Investigate Deforestation

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Intro to Color Imaging: Capturing and Reproducing Color

All types of color imaging, including film, print media, and modern digital cameras, reproduce color by gathering brightness data from a scene through different colored filters. The scene is then reproduced on paper, film, or screen by using this information to control the amounts of different colors of dyes, ink, or light.

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Primary colors

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Multispectral and hyperspectral imaging


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Deconstruct a Color Image

ImageJ has the ability to separate a color image into its component bands, or channels.

Download a color image

  1. Click the image below to open a window containing a full size version.
  2. Right-click (Win) or control-click (Mac) on the larger image and download the image file to your Week 11 folder.


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Deforestation in Bolivia

This image was taken by International Space Station astronauts on April 16, 2001. It shows deforestation patterns associated with the Tierras Bajas ("lowlands") project in eastern Bolivia. The spatial resolution is about 12 meters per pixel.

This area was originally dry tropical forest. As part of the Tierras Bajas project, people were resettled here from the Altiplano region to grow soybeans. The agricultural "pinwheels" are centered on small communities spaced 5 km apart. Roads connect the town centers.


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Launch ImageJ and open the image

  1. Launch ImageJ ImageJ Icon Small , choose File > Open, navigate to the Week 11 folder, and open the deforestation image you downloaded.
  2. Look at the image window status bar. What type of image is this, and how much memory does it occupy?
    reading_image_type_memory

    This is a 24-bit RGB (Red,Green,Blue) image, and it occupies 5.9MB of memory.


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Bit depth and color images

Most color images are 24-bit RGB images. 24 bits mean 3 separate 8-bit channelsrepresenting red, green, and blue light measurements across the scene. Since 8 bits allow us to encode 256 possible brightness values (from 0 to 255), this system is capable of producing any of 16.7 million different colors (256 x 256 x 256 = 16,777,216) for each pixel.

Color imaging systems can use any number of bits per pixel in each channel. Higher-end systems use 16 or more bits per pixel. ImageJ can work with 16-bit grayscale images, and can combine them into 48-bit color images. (You can do the math yourself, or peek at the answer belowover 281 trillion possible colors!)


    A 48-bit color system can represent 65536 x 65536 x 65536 = over 281 TRILLION colors!

  1. Mouse around the image and look at specific pixel values in the ImageJ window status bar.
  2. Recall from earlier in the course that the three values represent a color recipe for the pixelso many units of red light plus so many units of green light plus so many units of blue light. Your computer uses these numbers to control the brightness of the colored dots on your monitor at each location. (This applies to EVERYTHING you see on your computer - NOT just the digital images in ImageJ.)

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Separate the color channels

  1. Choose a specific pixelzoom in on the image if you want.
  2. Write down the pixel's coordinates (X,Y) and color values (R,G,B).
  3. Choose Image > Color > Split Channels. Your RGB color image separates into three grayscale images, in three different windows.
  4. Note the (red), (green), and (blue) added to the image window title bars.
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A memory puzzle explained

Note the size of the separate red, green, and blue channel images. Each is 1.5MB. Three of these add up to 4.5MB, but earlier you saw that the 24-bit RGB color image is about 6 MB. When ImageJ is working with 24-bit RGB images, it sets aside another "empty" channel in memory. In a program like Photoshop, this same image would occupy only 4.5 MB.


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Inspect the RGB pixel values

  1. Activate the red channel image (from the bottom of the Window menu), go to the pixel with the XY coordinates you wrote down (pan and zoom if necessary) and examine the pixel value (now a single number). How does this value compare to the Red value you wrote down for the pixel at the same coordinates in the RGB image?

    The pixel value from the red channel image should be the same as the first numberthe red value from the RGB image at the same coordinates.

    check_answer_rgb check_answer_red


  2. Predict the pixel values at the same coordinates in the green and blue channel images, then examine the images to check your predictions.

    The pixel values from the green and blue channel images should be the same as the G and B values at the same pixel location in the RGB image.



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Reconstruct a Color Image

ImageJ can also construct or reconstruct an RGB color image from the individual red, green, and blue channel images.

  1. Choose Image > Color > Merge Channels...
  2. Assign the red image to the red channel, the green image to the green channel, and the blue image to the blue channel. Nothing should be assigned to the Gray channel.
  3. Uncheck the Create Composite option and check the Keep Source Images option. color merge
  4. Click OK. The resulting image should look exactly like the one you started with in this activity.

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Optional (if you have time) - Make a False Color Image

This is cool and all, but why in the world would you want to deconstruct a color image? Here are a couple of ideas...

Make a false color image

What happens when you assign the images to the wrong channels?

  1. Close the reconstructed RGB color image you created in the previous section. The three grayscale color channel images should still be openif not, you'll have to open the RGB image and split the channels again.
  2. Choose Image > Color > Merge Channels...
  3. Assign different images to the red, green, and blue channels and create a new RGB image.
  4. Image reconstructed as blue, red, green.

    brg_image


  5. Try assigning the SAME image to all three channels. Predict what you think it will look like, then test your prediction.
  6. If you assign the same grayscale image to all three color channels, you will get a 24-bit grayscale image.

False color images are used to make specific features stand out so they are easier to see or interpret.

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Pump up the contrast

Due to atmospheric haze, most images shot from the International Space Station have poor contrast and flat colors. What can we do in ImageJ to punch up the contrast? The idea is to split the channels, manipulate them separately, and then merge the channels back together.

  1. Close any RGB images that are open. You should still have the red, green, and blue channel images open. (If not, you know what to do!)
  2. For each of the three channel images:
  3. Choose Image > Color > Merge Channels...
  4. Assign the appropriate images to the red, green, and blue channels and click OK.

    Compare this result to what you get when you auto-enhance the contrast of the original 24-bit RGB color image.

    contrast_merged



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