Lecture 2 Photographs and digital mages

Friday, 7 January 2011

Reading assignment:

Ch 1.5 data acquisition & interpretationCh 2.1, 2.5 digital imagingCh 3.3 scale

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What was covered in the previous lecture

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LECTURES• Jan 05 1. Intro previous• Jan 07 2. Images today

Jan 12 3. PhotointerpretationJan 14 4. Color theoryJan 19 5. Radiative transferJan 21 6. Atmospheric scatteringJan 26 7. Lambert’s LawJan 28 8. Volume interactionsFeb 02 9. SpectroscopyFeb 04 10. Satellites & ReviewFeb 09 11. MidtermFeb 11 12. Image processingFeb 16 13. Spectral mixture analysisFeb 18 14. ClassificationFeb 23 15. Radar & LidarFeb 25 16. Thermal infraredMar 02 17. Mars spectroscopy (Matt Smith)Mar 04 18. Forest remote sensing (Van Kane)Mar 09 19. Thermal modeling (Iryna Danilina)Mar 11 20. ReviewMar 16 21. Final Exam

Introduction

•Remote sensing•Images, maps, & pictures•Images and spectra•Time series images•Geospatial analysis framework•Useful parameters and units•The spectrum

Tuesday’s lecture was an introduction to remote sensingWe discussed:

what remote sensing wassomething about maps, images, and spectratime-series images - movieswhat 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)

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An imaging system

- scene

- optics

- (scan mirrors)

- focal plane

- detectors (film, CCD, etc.)

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Photographs

When it is enlarged enough, a photo gets fuzzy

A photo can be made incolor using dye layers

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Photographs utilize concentrations of opaque grains to represent brightnesses

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.

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Digital images

Each pixel is assigned a DN

0 200

198

168

199

75

100

100

100

75 75

75

168167

168

0 0 0

0

0

0

0 0

0

0

0

0

198

198

198

0 100 200 250

20

10

0

DN value

Num

ber

Histogram

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Digital images

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

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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 groundex: 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 metersLandsat 5 / ASTER NIR 30 metersASTER TIR 90 meters

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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

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IMAGE PROFILE

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 10 20 30 40 50

DISTANCE

SIG

NA

L

Important spatial properties in images (continued)

TWO POINT SOURCES

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50

DISTANCE

BR

IGH

TN

ESS

IMAGE PROFILE

0

0.1

0.2

0.3

0.4

0.5

0.6

0 10 20 30 40 50

DISTANCE

SIG

NA

L

Distance

Distance

DN

Bri

ghtn

ess

Two point sources

Image profile

Image profile: closer point sources

Distance

DN

° Resolution varies with object contrast, size, shape

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High contrast

Resolution, contrast & ‘noise’ affect detectability

Low contrast & blurred

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Low signal/noise

Large targets are more easily detected

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Blurred, no measurement error with ‘noise’

Recognition of shape is affected by resolving power

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Color information only, no spatial information (single pixel, three channels – Blue, , Green, & , & Red)

Resolution affects identification

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What can be said in B/W? What can be said about color alone?Where does most of the useful information come from?

Spectral information alone

Color information, no spatial information(single pixel, three channels – B, G, & R)

Spectrum – full “color” information,no spatial information

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What was covered in today’s lecture?

•Photographs and digital images•Structure of brightness elements in images•Detection•Resolution•Signal & noise•Point & extended targets

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Spatial data - photointerpretation & photogrammetry

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What will be covered in Tuesday’s lecture