4-2 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
4-4 Imaging Terms Swath Width Cross-Track Direction Along-Track
Direction Field-of-view Dwell Time
Slide 5
4-5 Areal Image Plane Imaging Optics Along-Track Direction
Cross-Track Direction Swath Width Platform Movement Scanning Mirror
Imaging Optics Point Detector Line Array Detectors Imaging Optics
(a) Framing Camera (b) Scanning System (c) Pushbroom System Types
of Imaging Systems
Slide 6
4-6 Comparison of Imaging Systems From Elachi,1987
Slide 7
4-7 Basic Telescope Primary Secondary Focal Plane
Slide 8
4-8 Diffraction Limited Resolution Circular Aperture Rayleigh
criterion for resolution:
Slide 9
4-9 Telescope Classification From Space Remote Sensing Systems:
An Introduction, by H.S. Chen, 1985
Slide 10
4-10 Types of Telescopes Newtonian Cassegrain Gregorian
Dall-Kirkham Ritchey-Critien Schwarzschild Schmidt From Space
Remote Sensing Systems: An Introduction, by H.S. Chen, 1985
Slide 11
4-11 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
Slide 12
4-12 Diffraction Limited Resolution Circular Aperture Rayleigh
criterion for resolution:
4-26 Space Mirror Materials From Space Remote Sensing Systems,
by H.S. Chen
Slide 27
4-27 Space Mirror Coatings Adapted From Space Remote Sensing
Systems, by H.S. Chen
Slide 28
4-28 Space Structural Materials From Space Remote Sensing
Systems, by H.S. Chen
Slide 29
4-29 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
Slide 30
4-30 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
Slide 31
4-31
Slide 32
4-32 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
Slide 33
4-33 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.
Slide 34
4-34 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
Slide 35
4-35 Detector Landscape > 1 mm100-1000 um10-100 um1-10
um0.1-1 um10-100 nm1-10 nm mmWaveSub-mmFIRMIRNIRVisUV TECHNOLOGIES
SC CalorimeterCCD Micro Channel Plate CMOS InGaAs Si: As QWIP InSb
SC Bolometer HEB SIS Schottky InP HEMTGaNGe: 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
Slide 36
4-36 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
Slide 37
4-37 CCD Readout
Slide 38
4-38 CCD Timing
Slide 39
4-39 Example: Kodak CCDs Device Pixels (HxV) Pixel Size (H x
Vm) 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
4-50 Analysis Techniques Ratio 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
Slide 51
4-51 Ratio Images
Slide 52
4-52 Ratio Images: Band 4/7 Highlights presence of clays due to
Al-OH bending mode absorption feature in band 7
Slide 53
4-53 Ratio Images: Band 3/1 Highlights presence of iron
oxides
Slide 54
4-54 Ratio Images: Band 4/3 Highlights presence of iron
oxides
Slide 55
4-55 Ratio Images: Color Combination
Slide 56
4-56 Analysis Techniques NDVI 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
4-61 Analysis Techniques Principal 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
4-65 Analysis Techniques Edge 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: 0 00 00 -2 0 4 0 00 0 00 4 0 00 00 0 4 0 0 0 00 0 4 0
00 0 4