1. Lecture 3 The Digital Image – Part I - Single Channel Data 12 September 2007 1

2. Main types of image analysis 2 Visual interpretation – advantages of being able to make use of the ability of the human mind to interpret differences in spatial patterns in an image. Digital analysis – advantages of being able to distinguish subtle differences in image brightness.

3. Steps along the way… 3 1. Obtain remote sensing data 2. Image rectification and restoration 3. Image enhancement 4. Image classification 5. Data merging and GIS integration

4. Key Points 4 1. Creating a single-channel (monochromatic) digital image  Recording of data detected by a remote sensing system – the use of bits  The relationship between bits and radiometric resolution  The Pixel  The Raster 2. Displaying the single channel digital image  Using gray scales  Importance of histograms  Contrast stretching 3. Issues in creating a map from a raster data set – imaging geometry issues

5. 5 EM Radiation EM Radiation Receptor Detector/ Converter digital number Recording device Figure 1

6. 6 EM energy detected Output Value Figure 2 Detector noise level

7. Description of number of signal increments – the use of “bits” 7 1. Digital media use binary digits (called bits) to record information – each bit has a value of either 1 or 0. 2. The number of bits represents the number of binary digits a media has for the storage of data, and determines the levels of information that can be recorded.

8. 8 Combinations for 1 bit storage 1, 0 = 2 possible combinations (= 2 1 ) Combinations for 2 bit storage 00, 01, 10, 11 = 4 possible combinations (= 2 2 ) Combinations for 3 bit storage 000,001,010,001,110, 101, 011, 111 = 8 possible combinations (= 2 3 ) If n = the number of bits available for storage, the 2 n = the number of levels of data that can be recorded

9. 9 Radiometric Resolution 8-bit (0 - 255) 8-bit 9-bit (0 - 511) 9-bit 10-bit (0 - 1023) 10-bit 0 0 0 Jensen, 2004 7-bit (0 - 127) 7-bit 0 The number of bits used to record the data by a remote sensing system defines the radiometric resolution of a system

10. 10 Example – Say that the sensor is set up to detect input values between 0 and 250 The values detected by the response of the sensor would range between 300 and 3900 Say that we use an 8 bit recorder= 256 levels (0 to 255) Then each level recorded by the sensor would represent 3600/256 = 14.1 levels of the response curve If we used a 10 bit recorder = 1024 levels (0 to 1023), then each level recorded by the sensor would represent 3600/1024 = 3.5 levels of the response curve The number of bits used in recording data defines the radiometric resolution of a remote sensing system – the larger the number of bits used in recording, the higher the radiometric resolution Figure 3

11. 11 EM Radiation EM Radiation Receptor Detector/ Converter digital number Recording device A single value recorded by a remote sensing is called a picture element or pixel

12. The Pixel - Definition 12  A two-dimensional picture element that is the smallest non-divisible element of a digital image.

13. Key elements of the Pixel 13  A pixel has a integer value (e.g., digital number) based on the number of bits used to record the data.  It has a dimension (in x,y space) that represents the area of the earth’s surface that generated the EM energy detected by the sensor

14. 14 Through a variety of different designs, multispectral scanning systems record spatially explicit information on digital representations of energy patterns. Other sensors employ similar scanning approaches to map the earth’s surface. Jensen 2007 Figure 4

15. An image is a Raster 15  Position of each pixel can be specified as:  Row and column position, e.g. Row 5, Column 6 (5, 6) Raster - a two-dimensional array of pixels or picture elements, which when displayed on a screen or paper, form an image. Figure 5

16. 16 Figure 6

17. 17 To create an image using a raster of data, you assign different shades of gray to the digital numbers. Figure 7

18. Digital images taken from space 18 Figure 8

19. 19 Image generated from Landsat Band 4 data collected from a region along the Mississippi River (from Mather 2005) This image is difficult to interpret because of the low contrast between features in the scene. What can be done to improve the contrast – Answer: Contrast Stretching!!!!! Figure 9

20. Frequency Distributions and Histograms 20  A tool that is used often in image analysis is the histogram which is defined as: A histogram is a graphical representation of a frequency distribution showing the class intervals horizontally (x-axis) and the frequencies vertically (y-axis)

21. 21 Figure 10

22. 22 Image generated from Landsat Band 4 collected from a region along the Mississippi River – No contrast stretch Mather 2005 Histogram generated from digital Landsat data Figure 10

23. 23 The small range of the digital numbers in the image raster leads to a small portion of the gray scale being used Figure 11

24. Contrast Stretching 24  Contrast stretching or enhancement is the process which expands the original input values from the remotely-sensed image to make use of the total range of sensitivity offered by the display device

25. Simple Linear Contrast Stretch Approach 25 1. Identify area on image for contrast stretch and produce histogram 2. Identify minimum and maximum value on histogram Example: V min = 7, V max = 57 Mather 2005 Figure 12

26. Simple Linear Contrast Stretch 26  On the image, for DN  64, set DN new = 255  On the image, for DN  7, set DN new = 0  All other DN values vary linearly between 1 and 254 Figure 13

27. 27 Input Digital Number Output Digital Number  7 0 84 99 1013 1118 1222 1326 1431 1535 1640 1744 1848 1953 2057 63251  64 255 Based on a linear regression, the computer creates a look-up table to convert Input DNs to Output DNs Figure 14

28. 28 Histogram from original data Mather 2005 Histogram generated using a simple linear contrast stretch Figure 15

29. 29 Original DataSimple Linear Contrast Stretch Mather 2005 Figure 16

30. 30 Mather 2005 Unstretched Simple Linear StretchTruncated Linear Stretch Histogram Equalization StretchGaussian Stretch Figure 17

31. 31 GEOMETRIC PROPERTIES OF A REMOTELY SENSED DATA SET The raster of numbers in a remote sensing image does not represent a cartographic reproduction of the area being imaged. Typically, the data record that contains the digital image contains a header file that presents the latitude and longitude of the four corners of the image, along with the size of the pixels. To create a geometrically accurate projection using the data, one must (amongst other things): a.Account for variations in the curvature of the earth b.Account for within scene variations of topography

32. Image Georectification 32  Image georectification involves remapping of the image raster data to account for biases in the x,y direction present in the data, which include: a. Earth curvature b. Variations in topography

33. 33 A satellite data raster was derived from scanning a surface that is curved, and therefore does not represent a true projection of that surface.

34. 34 In addition to geometric distortions introduced by earth curvature, variations in topography also introduce geometric biases into the image raster