1. 1 Pixel and Image Characteristics Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev Sede-Boker Campus 84990, ISRAEL Prof. Arnon Karnieli The Remote Sensing Laboratory Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev Sede-Boker Campus 84990, ISRAEL

2. 2 Pixel (picture element) A pixel having both spatial and spectral properties. The spatial property defines the "on ground" 2 dimensions. The spectral property defines the intensity of spectral response for a cell in a particular band.

3. 3 Pixel Value Digital number (DN) = Gray Level (GL) = Brightness Value (BV)

4. 4 Radiance to DN Optical system, detectors, electronics At sensor radiance DN (W m -2 sr -1  m -1 ) Integer (bit) The output (DN) is proportional to the input (at sensor radiance)

5. 5 A row of pixels A row of pixels represents a scan line collected as the sensor moves left to right or collected through the use of a linear array of photodetectors.

6. 6 An image An image is composed of pixels geographically ordered and adjacent to one another consisting of 'n' pixels in the x direction and ‘m' pixels in the y direction.

7. 7 One band When only one band of the EM spectrum is sensed, the output device (color monitor) renders the pixels in shades of gray (there is only one data set).

8. 8 Multispectral color composite Multispectral sensors detect light reflectance in more than one or two bands of the EM spectrum. These bands represent different data. When combined into the red, green, blue guns of a color monitor, they form different colors.

9. 9 True Color Composite BlueGreenRedNIRSWIR1TIRSWIR2

10. 10 False Color Composite BlueGreenRedNIRSWIR1TIRSWIR2

11. 11 SWIR Color Composite BlueGreenRedNIRSWIR1TIRSWIR2

12. 12 Multispectral image A multispectral image is composed of 'n' rows and 'n' columns of pixels in each of three or more spectral bands. There are in reality more than one "data set" which makes up one image. These different data sets are referred to as spectral bands, bands, or channels.

13. 13 Resolutions Resolutions: Spatial Radiometric Spectral Temporal Resolution - The smallest observable (measurable) difference.

14. 14 Spatial resolution “A measure of the smallest angular or linear separation between two objects that can be resolved by the sensor” Resolving power in the ability to perceive two adjacent objects as being distinct Depends on: - size - distance - shape - color - contrast characteristics - sensor characteristics

15. 15 Instantaneous Field of View (IFOV) Instantaneous field of view (IFOV) is the angular field of view of the sensor, independent of height IFOV is a relative measure because it is an angle, not a length.

16. 16 Field of View (FOV) Instantaneous Field of View (IFOV) = Pixel Flight direction

17. 17 GIFOV Ground projected Instantaneous Field of View (GIFOV) GIFOV depends on satellite height (H) H

18. 18 Line-pairs per unit distance

19. 19 Resolution target

20. 20 Resolution target 2 m4 m

21. 21 Different spatial resolutions 10 m 80 m 40 m 20 m

22. 22 Different spatial resolutions 1,000 m 30 m 3 m 300 m

23. 23 Contrast and shape

24. 24 Shadow Mountain Eye Project Ninety 61 cm mirrors, 2.25 km across.

25. 25 Common spectral sensors Landsat MSS - 80 m NOAA-AVHRR - 1,100 m Meteosat - 5,000 m Other sensors:

26. 26 Scale Scale - mathematical relationship between the size of objects as represented on maps, aerial photographs, or images. Measured as the ratio of distance on an image to the equivalent distance on the ground. Example: 1:50,000 1 cm on the map represents 50,000 cm or 0.5 km on the ground

27. 27 Radiometric resolution Number of digital levels that a sensor can use to express variability of brightness within the data Determines the information content of the image The more levels, the more details can be expressed Determined by the number of bits of within which the digital information is encoded

28. 28 Gray levels

29. 29 Gray levels

30. 30 Gary levels histogram

31. 31 Different Gray Levels 8 bit - 256 levels 2 bit - 4 levels3 bit - 8 levels 4 bit - 16 levels6 bit - 64 levels 1 bit - 2 levels

32. 32 Looking within the Shadowed Area

33. 33 Cloud Shadow The features under cloud shadow are recovered by applying a simple contrast and brightness enhancement technique. Part of the IKONOS (11-bit acquisition level) image is under cloud shadow. It can be recovered due to high radiometric resolution.

34. 34 Dynamic range Dynamic Range

35. 35 Spectral resolution Spectral Resolution The width and number of spectral intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive. Allows characterization based on geophysical parameters (chemistry, mineralogy, etc.)

36. 36 Multi- Super- Hyper- Ultraspectral Multispectral: 3 – 10 spectral bands (Landsat-TM, SPOT- HRV, NOAA-AVHRR)  Currently the most common systems Surperspectral: 10 – 100 spectral bands (MODIS, MERIS, Venµs)  Become more popular in recent years Hyperspectral: A few hundreds of spectral bands (AVIRIS, Hyperion);  Near-future development Ultraspectral: A few thousands of spectral bands.  Far-future development

37. 37 Multi- Hyper- Ultraspectral

38. 38 Signal to Noise Ratio Sensor responds to a both target brightness (signal) and electronic errors from various sensor components (noise) signal = the actual energy reaching the detector noise = random error in the measurement (all systematic noise has been removed) SNR = signal to noise ratio = Signal/Ratio To be effective, sensor must have high SNR

39. 39 Signal to Noise Ratio Laboratory Kaolinite spectrum convolved in various signal to noises

40. 40 Signal to Noise Ratio

41. 41 Signal to Noise Ratio LandsatALI

42. 42 Hyperspectral concept

43. 43 AVIRIS

44. 44 Spectral Cube

45. 45 1/ Temporal Resolution Temporal resolution - the frequency of data acquisition over an area Depends on: - the orbital parameters of the satellite - latitude of the target - SWATH width of the sensor - pointing ability of the sensor Also called “revisit time”

46. 46 SWATH 175 km 2800 km

47. 47 Tilting Capability

48. 48 Importance High temporal resolution is important for: - infrequent observational opprtunity (e.g., when clouds often obscure the surface) - short-lived phenomenon (floods, oil spills, dust storms, etc.) - rapid response (fires, hurricanes) - detection changes properties of a feature to distinguish it from otherwise similar features (phenology)

49. 49 Summary (1)

50. 50 Summary (2)

51. 51 Temporal vs. Spatial Resolution

52. 52 DN to Radiance (1) Pixel values (DNs) are scaled to byte values: L λ = "gain" * DN + "offset" where: L λ = Spectral radiance at the sensor’s aperture in watts/(meter 2 *ster*µm) "gain" = Rescaled gain in watts/(meter 2 *ster*µm) "offset"= Rescaled bias in watts/(meter 2 *ster*µm) “gain” and “offset” values are provided with the image.

53. 53 DN to radiance (2) Which is also expressed as: Where: Lmin λ = the spectral radiance that is scaled to DNmin in watts/(m 2 * ster * µm) Lmax λ = the spectral radiance that is scaled to DNmax in watts/(m 2 * ster * µm) DNmin = the minimum quantized calibrated pixel value (corresponding to Lmin λ ) in DN = 0 Dnmax = the maximum quantized calibrated pixel value (corresponding to Lmax λ ) in DN = 255

54. 54 Spectral radiance range Lmin, Lmax = radiance in w m -2 st -1  m -1 Example for the Landsat ETM+ sensor, high gain, after July 1, 2000

55. 55 Radiance to reflectance Where:  p = Unitless planetary reflectance L = Spectral radiance at the sensor's aperture d = Earth-Sun distance in astronomical units from nautical handbook ESUN = Mean solar exoatmospheric irradiances  s = Solar zenith angle in degrees

56. 56 Tables