1. Lecture 2 Photographs and digital mages Friday, 7 January 2011 Reading assignment: Ch 1.5 data acquisition & interpretation Ch 2.1, 2.5 digital imaging Ch 3.3 scale 1

2. What was covered in the previous lecture 2 LECTURES Jan 051. Introprevious Jan 072. Imagestoday Jan 123. Photointerpretation Jan 144. Color theory Jan 195. Radiative transfer Jan 216. Atmospheric scattering Jan 267. Lambert’s Law Jan 28 8. Volume interactions Feb 029. Spectroscopy Feb 0410. Satellites & Review Feb 0911. Midterm Feb 1112. Image processing Feb 1613. Spectral mixture analysis Feb 1814. Classification Feb 2315. Radar & Lidar Feb 2516. Thermal infrared Mar 0217. Mars spectroscopy (Matt Smith) Mar 0418. Forest remote sensing (Van Kane) Mar 0919. Thermal modeling (Iryna Danilina) Mar 1120. Review Mar 1621. Final Exam Introduction Remote sensing Images, maps, & pictures Images and spectra Time series images Geospatial analysis framework Useful parameters and units The spectrum

3. Tuesday’s lecture was an introduction to remote sensing We discussed: what remote sensing was something about maps, images, and spectra time-series images - movies what was to be covered in this class Today we discuss imaging systems and some of their characteristics Specialized definitions: scene the real-world target or landscape image a projection of the scene onto the focal plane of a camera picture some kind of representation of the image (e.g., hard copy) 3

4. An imaging system - scene - optics - (scan mirrors) - focal plane - detectors (film, CCD, etc.) 4

5. Photographs When it is enlarged enough, a photo gets fuzzy A photo can be made in color using dye layers 5 Photographs utilize concentrations of opaque grains to represent brightnesses

6. Digital Images CCD  silicon wafer  solid-state electronic component  array of individual light-sensitive cells  each = picture element (“pixel”) Each CCD cell converts light energy into electrons. A digital number (“DN”) is assigned to each pixel based on the magnitude of the electrical charge. A Charged Couple Device replaces the photographic film. In the case of digital cameras: Each pixel on the image sensor has red, green, and blue filters intermingled across the cells in patterns designed to yield sharper images and truer colors. 6

7. Digital images Each pixel is assigned a DN 0200 198 168 199 75 100 75 168167 168 000 0 0 0 00 0 0 0 0 198 0 100 200 250 20 10 0 DN value Number Histogram 7

8. Digital images When it is enlarged, a digital photo gets ‘pixilated’ Enlargement 8

9. Important spatial properties in images ° Field of view (“FOV”) - Distance across the image (angular or linear) ° Pixel size - Instantaneous Field of view (“IFOV”) Size in meters or is related to angular IFOV and height above ground ex: 2.5 milliradian, at 1000 m above the terrain 1000 m * (2.5 * 10 -3 rad) = 2.5 m Each pixel represents a ~square area in the scene that is a measure of the sensor's ability to resolve objects Examples: Landsat 7 / ASTER VIS15 meters Landsat 5 / ASTER NIR30 meters ASTER TIR90 meters 9

10. Radians defined Radian is a measure of angle, like degrees The circumference of a circle = 2  r, where r is its radius. There are 2  radians in a circle and 360 degrees A radian is therefore a little over 57 degrees 2.5 milliradians = 0.143 degrees 10

11. Important spatial properties in images (continued) Distance DN Brightness Two point sources Image profile Image profile: closer point sources Distance DN ° Resolution varies with object contrast, size, shape 11

12. High contrast Resolution, contrast & ‘noise’ affect detectability Low contrast & blurred 12 Low signal/noise

13. Large targets are more easily detected 13 Blurred, no measurement error with ‘noise’

14. Recognition of shape is affected by resolving power 14

15. ,, & Color information only, no spatial information (single pixel, three channels – Blue, Green, & Red) Resolution affects identification 15 What can be said in B/W? What can be said about color alone? Where does most of the useful information come from?

16. Spectral information alone Color information, no spatial information (single pixel, three channels – B, G, & R) Spectrum – full “color” information, no spatial information 16

17. What was covered in today’s lecture? Photographs and digital images Structure of brightness elements in images Detection Resolution Signal & noise Point & extended targets 17

18. Spatial data - photointerpretation & photogrammetry 18 What will be covered in Tuesday’s lecture