ee/ae 157a week 4: visible and near ir

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EE/Ae 157a

Week 4: Visible and Near IR

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Topics to be Covered

• Space Mirrors– Diffraction limited resolution, Space mirror materials, Mirror coatings,

structural materials• Space Detectors

– Photoemissive, Photoconductive, Photovoltaic, CCDs• Examples of Systems

– Landsat MSS and TM, SPOT• Examples of Image Artifacts

– Line Dropouts, Banding, Line Offsets• Analysis Techniques

– Ratio images, Principal components, NDVI, Edge enhancements, Sharpening, Spectral unmixing, Classification

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Basic Remote Sensing System

Source

Scattering Object

Waves Emitted

Collecting Aperture

Detector

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Imaging Terms

SwathWidth

Cross-TrackDirection

Along-TrackDirection

Field-of-view

Dwell Time

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Areal Image Plane

Imaging Optics

Along-Track Direction

Cross-Track DirectionSwathWidth

PlatformMovement

ScanningMirror

ImagingOptics

“Point”Detector

Line ArrayDetectors

Imaging Optics

(a) Framing Camera (b) Scanning System

(c) Pushbroom System

Types of Imaging Systems

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Comparison of Imaging Systems

Type Advantage Disadvantage

Film framing camera Large image formatHigh information densityCartographic accuracy

Transmission of filmPotential image smearing

Electronic framing camera Broad spectral rangeData in digital formatGood geometric fidelity

Difficulty in getting large arraysWide field-of-view optics

Scanning systems Simple detectorNarrow field-of-view opticsWide sweep capabilityEasy to implement multiplewavelenghts

Low detector dwell timeMoving partsDifficult to achieve goodgeometric fidelity

Pushbroom imagers Long dwell time per detectorGood cross-track geometricfidelity

Wide field-of-view optics

From Elachi,1987

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Basic Telescope

Primary

Secondary

Focal Plane

f FDf focal lengthF focal ratioD aperture diameter

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Diffraction Limited ResolutionCircular Aperture

1.22D

Dd 44.2

Rayleigh criterion for resolution:

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Telescope Classification

Telescope Primary Mirror Secondary Mirror

Cassegrain Parabola Hyperbola

Gregorian Parabola Ellipse

Ritchey-Chritein Hyperbola Hyperbola

Dall-Kirkham Ellipse Sphere

Newtonian Parabola Flat

Schmidt Aspheric Sphere

Scwarzschild Sphere Sphere

From Space Remote Sensing Systems: An Introduction, by H.S. Chen, 1985

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Types of Telescopes

Newtonian

Cassegrain

Gregorian

Dall-Kirkham

Ritchey-Critien

Schwarzschild

Schmidt

From Space Remote Sensing Systems: An Introduction, by H.S. Chen, 1985

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Telescope Terms

• Focal Length: Effective length of the light path from the lens or mirror to the focus point

• Aperture Size: Unobstructed size of the lens or mirror• Focal plane: The area covered with sensors that

change electromagnetic energy into electrical signals• Field of View: The angle viewed by the focal plane• Pixel Field of View: The angle viewed by a single

detector in the focal plane• Field of Regard: The total angle that a scanning

telescope can image

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1.22D

d 2.44D

Rayleigh criterion for resolution:

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Separation < 1.22 /D Separation = 1.22 /D Separation > 1.22 /D

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Diffraction Limited ResolutionEffect of Wavelength

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Diffraction Limited ResolutionAperture Size for Constant Resolution

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0.1 10 1000 100000 10000000Wavelength in Microns

Aper

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Diffraction Limited ResolutionEffect of Apodization

NO APODIZATION GAUSSIAN SIGMA = RADIUS

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Diffraction Limited ResolutionEffect of Apodization

NO APODIZATION GAUSSIAN SIGMA = RADIUS

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Diffraction Limited ResolutionEffect of Aperture Shape

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Diffraction Limited ResolutionEffect of Surface Errors

NO ERRORS WAVELENGTH / 10

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NO ERRORS WAVELENGTH / 10

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NO ERRORS WAVELENGTH / 6.66

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Improving Angular Resolution Through Aperture Synthesis

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LIGHT ADDED IN PHASE

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LIGHT ADDED OUT OF PHASE

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Aperture SynthesisEffect of Aperture Spacing

Spacing = 4 diameters Spacing = 8.5 diameters

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Space Mirror Materials

Material Density Modulus of Elacticity(N/ cm2 x 106)

Coefficient of ThermalExpansion(1/ oC x 10-6)

Fused Silica 2.20 7.0 0.55

ULE 2.21 6.74 0.03

Cer-Vit 2.50 9.23 0.1

Zerodur 2.52 9.20 0.05

Beryllium 1.86 28.0 12.4

Aluminum 2.70 6.9 23.9

Invar 8.0 14.8 1.3

Graphite Epoxy 1.72 6.89 1.0

From Space Remote Sensing Systems, by H.S. Chen

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Space Mirror Coatings

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AluminumSilverGoldCopper

Adapted From Space Remote Sensing Systems, by H.S. Chen

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Space Structural Materials

Characteristics Al Be Gr/ Ep Gr/ Al Gr/ Mg

Light Weight Fair Fair Good Good Good

High Modulus Fair Good Fair Good Good

No Outgassing Fair Fair Poor Poor Poor

Conductivity Fair Fair Poor Fair Fair

Cost Low Medium Medium High High

From Space Remote Sensing Systems, by H.S. Chen

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Detectors

• Electro-optical detectors transforms wave energy into electrical energy

• The two most common types are thermal and quantum detectors• Thermal detectors rely on the increase in temperature in heat

sensitive material due to absorption of incident radiation• Implementations include bolometers and thermocouplers• Thermal detectors are slow, have low sensitivity, and their

response is independent of wavelength• Thermal detectors are not commonly used in modern remote

sensing systems

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Detectors

• Quantum detectors use the direct interaction of the incident photons with the detector material, which produces free charge carriers

• They are typically classified into three categories: photoemissive, photoconductive, and photovoltaic

• Quantum detectors have fast response and high sensitivity, but have a limited spectral response

• Quantum detectors are characterized by a parameter

D* A f

NEP

A Detector areaf Bandwidth

NEP Noise equivalent power

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D*

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Photoemissive Detectors

• In photoemissive detectors, the incident radiation leads to electron emission from a photosensitive intercepting surface

• The emitted electrons are accelerated and amplified• These detectors are primarily used at shorter wavelengths, since

the incoming photons must have sufficient energy to overcome the binding energy of the electrons

• Cesium has a cut-off wavelength of 0.64 microns• Composites, such as silver-oxygen-cesium have longer

wavelength (1.25 microns) cut-off wavelength• An example of this type of detector is the Photomultiplier tube

(PMT)• Landsat multi-spectral scanner (MSS) used PMT detectors for

three of the four bands

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Photoconductive Detectors

• In photoconductive detectors, photons with incident energy greater than the forbidden band energy gap in the semiconductor material produces free-charge carriers

• This causes the resistance of the photosensitive material to vary inversely proportional to the number of incident photons

• Exciting electrons across the forbidden band requires substantially less energy than electron emission, and consequently photoconductive detectors can operate at longer wavelengths

• Back-biased silicon photodiodes operate in the photoconductive mode

• Photodiodes can respond within a few nanoseconds• Landsat MSS band 4 used a photodiode as a detector.

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Photovoltaic Detectors

• In the case of photovoltaic detectors, the incident energy is focused on a p-n junction, modifying the electrical properties, such as the backward bias current

• Unbiased silicon photodiodes operate in the photovoltaic mode• Because this mode has no dark current, it has distinct advantages

for low-level dc radiation signals• The photovoltaic response time is typically limited to a few

microseconds

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Detector Landscape> 1 mm100-1000 um10-100 um1-10 um0.1-1 um10-100 nm1-10 nm

mmWaveSub-mmFIRMIRNIRVisUV

TECHNOLOGIES

SC Calorimeter CCD

Micro Channel Plate

CMOS

InGaAs

Si: As

QWIP

InSb

SC Bolometer

HEB

SIS

Schottky

InP HEMTGaN Ge: GaSi: Sb

HgCdTe

CCD Calorimeter

Uncooled Bolo

Commercial and defense applications in terrestrial imaging and sensing• strong technical infrastructure• synergistic funding

Commercial and defense applications in comms and radar

Primarily driven by space based astrophysics• weak infrastructure• limited funding • great science

SAFIR

• strong technical infrastructure

• synergistic funding

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Charge Coupled Device (CCD) Detectors

• CCD devices control the movement of signal electrons by the application of electric fields

• Most CCD devices can operate in either the photoconductive or the photovoltaic modes

• In monolithic CCDs the photon detection and multiplexing are performed on the same chip. It is best suited for VLSI technology, and have lower production costs

• In hybrid CCDs these operations are performed by two separate chips. Splitting these operations means that each can be optimized separately

• CCD detectors are easily integrated into arrays• Most modern remote sensing systems use CCD detectors.

Examples include SPOT, MOMS and Galileo

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CCD Readout

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CCD Timing

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Example: Kodak CCDs

Device Pixels (HxV) Pixel Size (H x Vµm) KAF-0261E 512 x 512 20.0 x 20.0 KAF-0401E(/LE) 768 x 512 9.0 x 9.0 KAF-1001E 1024 x 1024 24.0 x 24.0 KAF-1301E(/LE) 1280 x 1024 16.0 x 16.0 KAF-1401E 1320 x 1037 6.8 x 6.8 KAF-1602E(/LE) 1536 x 1024 9.0 x 9.0 KAF-3200E(ME) 2184 x 1472 6.8 x 6.8 KAF-4301E 2084 x 2084 24.0 x 24.0 KAF-6303E(/02LE) 3088 x 2056 9.0 x 9.0 KAF-16801E(/LE) 4096 x 4096 9.0 x 9.0

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Landsat 7 Orbit

Parameter Value

Orbit Altitude, km 705.3

Orbit Period, min 98.9

Orbit Inclination, deg 98.2

Repeat Cycle, days 16

Orbit Type Sun synchronous

Image Time 10:00 a.m local time

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Landsat ETM+Parameter Value

Telescope Diameter, cm 40.6 Telescope Type Ritchey-Chritien f-number 6 Swath Width, km 185 Scan Frequency, Hz 7.0 Scan Angle, deg 7.7 Number of lines per scan 16 Ground Resolution, m 15/ 30 / 60 Bandpass, m Band 1 0.45-0.52 Band 2 0.52-0.60 Band 3 0.63-0.69 Band 4 0.76-0.90 Band 5 1.55-1.75 Band 6 10.4-12.5 Band 7 2.08-2.35 Band 8 0.52-0.90 (15m) Quantization Level 256 (8 bits)

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Landsat TM Optical System

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SPOT

Parameter Value

Orbit Altitude, km 822Orbit Type Sun synchronousImage Time 10:30 a.m.Swath Width, km 60Imager Type PushbroomNumber of detectors per line 6000 / 3000Detector Type CCD ArraysGround Resolution, m 10 / 20Bandpass, m Band 1 0.50-0.59 Band 2 0.61-0.69 Band 3 0.79-0.90 Band 4 0.50-0.90

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SPOT vs LANDSAT

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Analysis TechniquesColor Combinations

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Analysis TechniquesColor Combinations

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ASTER Parameters( m)Subsystem Band

No.Spectral Range Spatial

Resolution (m)

Quantization Levels(bits)

VNIR 1 0.52-0.60 15 8

2 0.63-0.69

3N 0.78-0.86

3B 0.78-0.86

SWIR 4 1.60-1.70 30 8

5 2.145-2.185

6 2.185-2.225

7 2.235-2.285

8 2.295-2.365

9 2.360-2.430

TIR 10 8.125-8.475 90 12

11 8.475-8.825

12 8.925-9.275

13 10.25-10.95

14 10.95-11.65

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ASTER DATA OF CUPRITE, NV

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ASTER Color Combinations

1 - 2 - 3 1 - 3 - 6

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Analysis TechniquesRatio Images

• Ratio images are formed by dividing the data value in one band by that of another band

• Ratio images are used to emphasize differences in spectral reflectance of materials. For example, vegetation shows a maximum reflectance in TM Band 4 and a lower reflectance in band 2. The ratio image 4/2 enhances the vegetation signature

• Ratio images minimize the difference in illumination conditions, and suppress the effects of topography

• A disadvantage is that ratio images suppress differences in albedo; materials with different albedos but similar spectral properties may not be distinguishable in ratio images

• Another disadvantage is that noise is emphasized in ratio images

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Ratio Images

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KaoliniteAluniteHematiteMontmorilloniteJarositeGoethiteBuddingtoniteMuscovite

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Ratio Images: Band 4/7

Highlights presence of clays due to Al-OH bending mode absorption feature in band 7

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Highlights presence of iron oxides

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Highlights presence of iron oxides

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Ratio Images: Color Combination

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Analysis TechniquesNDVI

• The normalized difference vegetation index (NDVI) is defined as

• Higher values of NDVI indicate higher concentration of green vegetation

• NDVI maps are typically calculated using biweekly combinations of images to reduce the effects of cloud cover

NDVI nir red

nir red

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Analysis TechniquesNDVI

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Analysis TechniquesIntensity/Hue/Saturation Transformation

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Analysis TechniquesIntensity/Hue/Saturation Transformation

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Analysis TechniquesSensor Combinations

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Analysis TechniquesPrincipal Components

• Typically, images from individual bands are highly correlated on a pixel by pixel basis

• The principal component transformation arranges images in order of the amount of variance in the data across the image

• This mathematical transformation is similar to calculating the eigenvalues and eigenvectors of the image on a pixel by pixel basis

• Most of the variance is typically in the first few principal components, with the last few dominated by noise

• The first PC image is typically dominated by topographic effects• By displaying three PC images as red, green and blue, spectral

variations are typically enhanced

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1 – 2 - 3 PC1 – PC2 – PC3

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Analysis TechniquesEdge Enhancements

• Edge enhancement filters are used to enhance linear features in images

• Geologists use linear features to map faults, while geographers use linear features to identify man-made structures such as roads

• Edges can be enhanced using non-directional or directional filters• An example of a non-directional filter is the Laplace kernel

• Directional edge enhances are used to identify linear features in specific directions:

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Analysis TechniquesEdge Enhancements

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Analysis TechniquesSupervised Classification

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Analysis TechniquesUnsupervised Classification

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Spectral Unmixing

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Spectral Unmixing

ee/ae 157a week 4: visible and near ir

Documents

space mirror materials

aperture synthesis light

space mirror coatings

angular resolution

space structural materials

comparison of imaging

scanning telescope

unobstructed size