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Understanding Digital Images: Did you ever go to a football game where the fans, sitting in a special section of the arena, were assigned special cards? These cards are numbered in sequence and assigned to fans sitting in specific seats. When given the signal, the fans hold up their cards over their heads. The effect is to produce an image that can be seen from across the stadium. The individual cards contain only a small piece of the total image. It takes all of the cards held up together to produce the full image. Digital images are very much like the stadium cards. Instead, digital images are made up of many, many pixels (picture elements). When you view a digital image using your computer, it is like looking at the stadium cards from across the stadium. (Another analogy would be the individual tiles that makeup a mosaic.) If you have a pair of binoculars in the stadium you can zoom in and see individual fans holding up their cards. In a computer program, you can zoom in on the image and see individual pixels. Computers have changed the way we look at images. We can have the computer leave out certain pixels (crop), or zoom in on certain pixels (magnify), or change a specific color into a new color. Each pixel can be assigned a number value (RGB or HSV) and the number data transferred through the Internet, telephones, or satellites. Stacks of images can be stored as animations and displayed at the movies or on your television or computer monitor. When a computer displays a digital image, it looks at the correct “column, row, and seat number” assigned to each pixel. It places each pixel in its correct location. A row of pixels is referred to as a raster. (Images made up of pixels are sometimes referred to as raster graphics.) Next, the computer assigns a color based on the number value contained within each pixel. The result is an image on your screen. Not all digital images are processed the same, which explains why some images are in the “jpg” format while others are in the “gif.” If you print out a digital image, the information is sent to the printer which carries out a similar process. Not all digital images are the same quality. The quality of the image can be a factor of the image’s resolution. Resolution is measured by how large or small the pixels are. Generally, the smaller an image’s pixels, the higher the resolution. Another way of looking at it is that a high resolution image has more pixels. So if more pixels mean higher resolution, why aren’t all digital images high resolution? Is there ever a time when you may need an image with a lower resolution? It usually comes down to file size. Digital images can become very large in the amount of computer memory it takes to store and transmit them. Large digital images may slow down transmission through faxes and the Internet. The total number of pixels in an image is only one factor in calculating file size, the other is color depth. Color depth is the term that refers to how the computer processes the color information contained in the pixel. Color depth is usually referred to in units of bits, the basic unit of computer memory (24-bit color, 8-bit color, 8-bit grayscale, 1-bit black and white). The more bits/pixel an image contains, the more computer memory it requires. The size of the digital image is referred to as Raw File Size and is calculated by multiplying the number of pixels by the bits. To further complicate the topic, computer programs often compress or “squeeze out” unnecessary information from an image to make the file size smaller. Compression is fine to help images load onto your Internet page faster, but file compression may have unwanted results if you want to analyze the image in a scientific way. Humans see in the visible range of the electromagnetic spectrum. Our ordinary digital cameras are sensitive to the colors of light that are reflecting off our subjects. It should be mentioned here that not all cameras (sensors) “see” visible light. Many scientific instruments utilize wavelengths of energy such as Infrared, Ultraviolet and X-rays to produce digital images. These images contain useful information such as temperature, elevation and moisture content. These special sensors or cameras can be attached to telescopes, satellites, or microscopes and help us to visualize the world from afar. This leads us to our next topic: Remote Sensing. Remote Sensing Remote sensing is the ability to analyze and measure phenomena from a distance. Various types of instruments are used to obtain information, usually in the form of a digital photograph or digital image. Each instrument used to gather information varies in the type of sensors it uses. Instruments may measure wavelengths anywhere in the electromagnetic spectrum. The type of sensor being used will directly affect the spatial resolution of the image, The greater the resolution the better one is able to measure and observe from a distance. Some remote sensing allows us to visualize things far away (Satellites, Telescopes, GPS) while other instruments allow us to look deep inside (X-ray, MRI, Electron Microscopy). Advances in science are further redefining our definition of remote sensing with new techniques such as DNA fingerprinting, electrophoresis, and molecular spectroscopy. Even everyday technology such as TV, radio, Internet and cell phones can be considered forms of remote sensing. While remote sensing allows us to gather information from a distance, image processing allows us to use computers to analyze these images or data. Although the image may look like a black & white or color photograph, the colors or other wavelengths are represented by digital numbers (DN). These numbers make up the digital image. Digital images convert the information (color, temperature, etc.) into a number. Once the image has been assigned a number, it is displayed on the monitor as a pixel value. This pixel is the dot of color which gives the image its structure. We will use Scion Image software to analyze and process the digital numbers (DN) contained in digital images. The software will allow you to analyze digital images based upon the differences in the numbers (DN). Some of the techniques possible with Scion Image are: measuring, counting, density slicing, particle analysis, animation, and analyzing Digital Elevation Maps (DEM), and Pseudo-coloring. (Scion Image is a very powerful software package and is capable of many different processing techniques. For additional information refer to the user manual that comes with Scion Image software. The following techniques only serve as an introduction) Measuring: Measuring distance and area are easily accomplished using satellite images and Scion Image software. Each pixel on a NOAA weather satellite image is 16 miles square (4 miles x 4 miles). We know this because scientists know the distance from earth to the satellite and the resolution of the sensors used in the satellite. When the Scion Image software is calibrated (Set Scale) to the known size of one pixel, it will then calculate values using measuring tools. Scientists can measure speed, direction and size of storms. Oil spills can be seen from space and the rate of spread calculated. Doctors can measure the size and growth of tumors using MRI or other medical images. Histograms: The histogram of an image is a statistical graph showing the number of shades of gray (DN/pixel value) and the frequency at which they occur in the image. In other words, a histogram (graph) is produced where the height of a bar, above each pixel value, is proportional to the number of occurrences of each pixel with that value. Plotting a histogram would let a scientist calculate the most common temperature in an area or the average elevation in a mountain range. Density Slicing: Density slicing gives you the ability to highlight a range of pixel values using the LUT (Look Up Table) in the image. Pixel values range from 0 (absolute white) to 255 (absolute black). Once the pixel values of interest are colorized, every pixel in the image will standout clearly among the others in the image. Simply, density slicing makes some areas of the image more easily seen by assigning them a unique color. One application would be to make all the areas of a particular temperature be one assigned color. In addition to being able to see parts of an image more clearly, density slicing also allows us to quickly isolate some parts of an image from others. Once the area is isolated, measurements can be made using particle analysis of the area. Doctors use density slicing to visually isolate a tumor and then measure the area. Repeating this procedure over time allows the doctors to calculate change in growth. Particle Analysis: Particle analysis will allow one to highlight certain pixel values and take measurements. The pixels in question can be isolated manually, or by using density slicing. The computer program is calibrated and the known values are applied to the isolated pixels. Density Calibrating an Image: Sometimes scientists know values for some pixels and not for others. The values of the known pixels can help scientists to calculate the unknown values. One example is temperature. If scientists know the temperature in two different areas, such as Raleigh, NC and in New York City, they can produce a linear regression plot to calculate temperatures throughout the range. The same idea could also be applied in measuring altitude (Digital Elevation Maps). Density calibrating an image will mathematically relate the values of one pixel to other pixels. Digital Elevation Maps: Some remote sensors are capable of producing and displaying elevation (Digital Elevation Maps or DEM). These images may appear fuzzy or out of focus. Looking at a DEM only with our eyes provides little useable information. Computer programs such as Scion Image are capable of producing “3D” images using DEM’s to help us see the mountains or the valleys. These 3D images can be relative (qualitative) or quantitative when the correct scale is set in the computer program. Scientist’s could calculate the surface topography of the ocean floor or the surface of a distant planet using digital elevation maps. Animation: Animations can be produced by sequencing a number of satellite or other images into one long repetitive file. Using computer generated animations allows use to see changes in weather patterns, movements of oil slicks, melting of glaciers, or deforestation over time. Almost every local TV weather broadcast includes short animations of how storms are moving. One important application of quantified animations is in making predictions. When will the oil slick strike the shore? Where will the hurricane strike land? It is also possible to take an animation and “un-stack” it into its individual frames. Scientists can study and analyze more carefully how processes are occurring. Sometimes the individual frames can be colorized or quantified and then “re-stacked” back into an animation. The new animation provides information we could not have seen before. Weather patterns are often colorized to show temperature or precipitation. Pseudo-color or False Color Images: Many of the remote sensors produce digital images that are grayscale. In a grayscale image pixel values range from 0 (absolute white) to 255 (absolute black). Computer programs such as Scion Image can “reassign” new colors to the pixels. Scion Image uses a LUT (color Look Up Table) tool to assign colors to the range of pixels. We see pseudo-color images everyday. Most newspapers have a weather map of the United States with temperatures displayed as color (the warmer areas are red and the colder areas are blue).