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4-1 EE/Ae 157a Week 4: Visible and Near IR

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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

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4-3 Basic Remote Sensing System Source Scattering Object Waves Emitted Collecting Aperture Detector

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4-4 Imaging Terms Swath Width Cross-Track Direction Along-Track Direction Field-of-view Dwell Time

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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

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4-6 Comparison of Imaging Systems From Elachi,1987

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4-7 Basic Telescope Primary Secondary Focal Plane

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4-8 Diffraction Limited Resolution Circular Aperture Rayleigh criterion for resolution:

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4-9 Telescope Classification From Space Remote Sensing Systems: An Introduction, by H.S. Chen, 1985

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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

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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

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4-12 Diffraction Limited Resolution Circular Aperture Rayleigh criterion for resolution:

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4-13 Diffraction Limited Resolution Circular Aperture Separation < 1.22 /DSeparation = 1.22 /DSeparation > 1.22 /D

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4-14 Diffraction Limited Resolution Effect of Wavelength

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4-15 Diffraction Limited Resolution Aperture Size for Constant Resolution Aperture Size in meters

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4-16 Diffraction Limited Resolution Effect of Apodization NO APODIZATIONGAUSSIAN SIGMA = RADIUS

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4-17 Diffraction Limited Resolution Effect of Apodization NO APODIZATIONGAUSSIAN SIGMA = RADIUS

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4-18 Diffraction Limited Resolution Effect of Aperture Shape

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4-19 Diffraction Limited Resolution Effect of Surface Errors NO ERRORSWAVELENGTH / 10

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4-20 Diffraction Limited Resolution Effect of Surface Errors NO ERRORSWAVELENGTH / 10

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4-21 Diffraction Limited Resolution Effect of Surface Errors NO ERRORSWAVELENGTH / 6.66

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4-22 Improving Angular Resolution Through Aperture Synthesis

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4-23 Improving Angular Resolution Through Aperture Synthesis LIGHT ADDED IN PHASE

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4-24 Improving Angular Resolution Through Aperture Synthesis LIGHT ADDED OUT OF PHASE

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4-25 Aperture Synthesis Effect of Aperture Spacing Spacing = 4 diametersSpacing = 8.5 diameters

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4-26 Space Mirror Materials From Space Remote Sensing Systems, by H.S. Chen

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4-27 Space Mirror Coatings Adapted From Space Remote Sensing Systems, by H.S. Chen

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4-28 Space Structural Materials From Space Remote Sensing Systems, by H.S. Chen

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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

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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

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4-31

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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

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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.

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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

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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

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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

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4-37 CCD Readout

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4-38 CCD Timing

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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

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4-40 Landsat 7 Orbit

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4-41 Landsat ETM+

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4-42 Landsat TM Optical System

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4-43 SPOT

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4-44 SPOT vs LANDSAT

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4-45 Analysis Techniques Color Combinations

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4-46 Analysis Techniques Color Combinations

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4-47 ASTER Parameters SubsystemBand No. Spectral RangeSpatial Resolution (m) Quantization Levels (bits) VNIR10.52-0.60158 20.63-0.69 3N0.78-0.86 3B0.78-0.86 SWIR41.60-1.70308 52.145-2.185 62.185-2.225 72.235-2.285 82.295-2.365 92.360-2.430 TIR108.125-8.4759012 118.475-8.825 128.925-9.275 1310.25-10.95 1410.95-11.65

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4-48 ASTER DATA OF CUPRITE, NV

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4-49 ASTER Color Combinations 1 - 2 - 31 - 3 - 6

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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

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4-51 Ratio Images

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4-52 Ratio Images: Band 4/7 Highlights presence of clays due to Al-OH bending mode absorption feature in band 7

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4-53 Ratio Images: Band 3/1 Highlights presence of iron oxides

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4-54 Ratio Images: Band 4/3 Highlights presence of iron oxides

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4-55 Ratio Images: Color Combination

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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

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4-57 Analysis Techniques NDVI

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4-58 Analysis Techniques Intensity/Hue/Saturation Transformation

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4-59 Analysis Techniques Intensity/Hue/Saturation Transformation

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4-60 Analysis Techniques Sensor Combinations

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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

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4-62 Analysis Techniques Principal Components

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4-63 Analysis Techniques Principal Components

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4-64 Analysis Techniques Principal Components 1 2 - 3PC1 PC2 PC3

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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

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4-66 Analysis Techniques Edge Enhancements

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4-67 Analysis Techniques Supervised Classification

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4-68 Analysis Techniques Unsupervised Classification

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4-69 Spectral Unmixing

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4-70 Spectral Unmixing