Lecture 2 Photographs and digital mages Thursday, 7 January 2010 Lecture 2 Photographs and digital mages Reading assignment: Ch 1.5 Ch 2.1, 2.5 Ch 3.3 1

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) 2

- detectors (film, CCD, etc.) An imaging system - scene - optics - (scan mirrors) - focal plane - detectors (film, CCD, etc.) 3

Photographs Photographs utilize concentrations of opaque grains to represent brightnesses When it is enlarged enough, a photo gets fuzzy A photo can be made in color using dye layers 4

Digital Images A Charged Couple Device replaces the photographic film. 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. 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. 5

Digital images 200 198 168 199 75 100 167 Histogram Each pixel is assigned a DN 200 198 168 199 75 100 167 0 100 200 250 20 10 DN value Number Histogram 6

Digital images When it is enlarged, a digital photo gets ‘pixilated’ Enlargement 7

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 VIS 15 meters Landsat 5 / ASTER NIR 30 meters ASTER TIR 90 meters 8

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

Important spatial properties in images (continued) ° Resolution varies with object contrast, size, shape Two point sources Brightness Distance Image profile Image profile: closer point sources DN DN Distance Distance 9

Resolution and contrast affect detectability High contrast Blurred Low contrast Low contrast & blurred 10

Noise (measurement error) affects resolution and detectability 11

Target area affects resolution and detectability the human eye tends to discriminate based on mean brightness expressed in terms of the standard deviation of the mean (standard error), not the standard deviation of the population 12

Recognition of shape is affected by resolving power 13

Resolution affects identification Color information only, no spatial information (single pixel, three channels – Blue, Green, & Red) What can be said in B/W? What can be said about color alone? Where does most of the useful information come from? 14

Spectral information alone Spectrum – full “color” information, no spatial information Color information, no spatial information (single pixel, three channels – B, G, & R) 15

Photographs and digital images We covered: Photographs and digital images Structure of brightness elements in images Detection Resolution Signal & noise Point & extended targets Next lecture: Spatial data - photointerpretation & photogrammetry 16