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53 Cards in this Set

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What is the relationship between band stats (histograms) and contrast stretching?

know what it means to generate a histogram: a representation of a frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportional to the corresponding frequencies;

a graphical representation of the pixels exposed in your image histograms allow us to analyze extremely large datasets by reducing them to a single graph that can show primary, secondary, and tertiary peaks in data as well as give a visual representation of the statistical significance of those peaks you can connect the shape of a histogram with the mean and median of statistical data that you use to create it-- the relationship between mean and median can help predict shape of histogram how to plot band stats:
tools->Stats->compute stats->select input file->hit ok->choose histogram

stretching: altering the distribution of digital numbers changes the contrast; literally stretching the DNs to get what you want, since you only have 255 DNs.

what units are radiance measured in? what units are spectral radiance measured in?

radiance: watts / meter ^2 / steradian

spectral radiance: m^2 / steradian / hertz

what is a steradian?

basically, it's a direction

used to define the angle over which reflectance from the earth's surface is measured

unit for a solid angle of a sphere

defines the angle over which reflectance from the earth's surface is measured

what is the difference between irradiance and exitance?

irradiance: the radiant flux received by a surface per unit area

spectral irradiance: the irradiance of a surface per unit frequency or wavelength, depending on whether the spectrum is taken as a function of frequency or of wavelength

exitance: wikipedia says exact same thing, but radiant flux is emitted by a surface

spectral exitance is the radiant exitance of a surface per unit freq. or wl

how does the atmosphere affect radiance meausred by the sensor?

atmosphere acts as a reflector- adding a scattered extraneous path radiance to the signal detected by sensor

total radiance detected by a sensor: target/object radiance and atmospheric path radiance

the total radiance detected by a sensor is a combo of target/object radiance and atmospheric path radiance. Explain the three primary ways by which RS system receives radiation.

Is = radiance reflected from Earth's surface, conveys info about surface reflectance

Io=radiation scattered from solar beam directly to sensor without reaching Earth

Id = diffuse radiation, to E then to atmosphere then to sensor

(I = observed radiance at sensor)

why are brightness measurements in DNs or radiances not optimal?

because values are subject to modifications by differences in sun angle, atmospheric effects, angle of observation, and other effects that introduce brightness errors unrelated to the characteristics we wish to observe

why is reflectance more useful?

useful to use the proportion of radiation reflected from an object relative to the total amount of radiation incident upon that object. This proportion is useful for defining distinctive spectral characteristics of an object

reflectance = (energy reflected from target / energy incident upon the target) * 100

reflectance = observed brightness / irradiance

why can't reflectance be measured directly?

because normally we can only observe brightness and must estimate irradiance

what are two ways in which radiance can be converted to reflectance?

reflectance = (energy reflected from target / energy incident upon the target) * 100

reflectance = observed brightness / irradiance

strategies for estimating reflectance:
RS analyst has a measure of brightness for pixels at the lens of a sensor for a specific spectral channel (W/m2*sr*um)
measure brightness at ground level, just before incident radiation is redirected back to sensor
Estimate incident radiation using a calculation of exoatmospheric radiation for a specific time, data and place

data is delivered in DN values that can be converted to 'radiance at sensor' and then converted to "reflectance at sensor"/"apparent reflectance"

radiance or reflectance at sensor can be converted to radiance or reflectance (on the ground/surface/absolute) thru atmospheric correction
^^ i think this is what she wants you to understand

*Be able to describe the processes by which DNs recorded at the sensor are converted to At-Sensor Radiance, then At-Sensor Reflectance, the On-the-Ground Reflectance (aka Surface, absolute, or apparent reflectance)

data is delivered in DN values that can be converted to 'radiance at sensor' and then converted to "reflectance at sensor"/"apparent reflectance"

radiance or reflectance at sensor can be converted to radiance or reflectance (on the ground/surface/absolute) thru atmospheric correction

At-Sensor Radiance: conversion requires info about the gain and bias (offset) from the sensor

At-Sensor Reflectance: requires estimation of irradiance if not directly measurable;

On-the-Ground Reflectance: radiance or reflectance at sensor can be converted to on-the-ground thru atmospheric correction; measure brightness on ground level, just before incident radiation is redirected back to the sensor

Low and High Grain Modes (and bias)- what they are, when they are used, and found, and how they are used to convert to At-Sensor Radiance

Gain: spectral gain optimizes a sensor's senstivity and prevents saturation
slope = gain

high gain: surface brightess is low; measures lesser radiance range but increased sensor sensitivity

low gain: surface brightness is high; measures greater radiance range but decreased sensor sensitivity

gain settings can change based on land cover type and time of year

*When might it be necessary to convert images to radiance or reflectance

radiance and reflectance have physical values and are understandable
single date image: ratios or vegetation indices require multiple bands that have different callibrations
multiple images: different sensors and different times

What are two ways in which images can be atmospherically corrected

atmospheric effects in the imagery corrected by calculating optical thickness, atmospheric transmittance, spectral solar irradiance at the top of the atmosphere, etc.
requires atmospheric models and/or in situ atmospheric data collection

results in 'on the ground' or 'surface' or 'absolute' reflectance values

necessary when detection of subtle diffs in reflectance spectra is necessary, compare two images of the same area from diff dates or compare to spectral libraries or field spectral data

not necessary when relative diffs between responses from soil, water, veg, etc. are strong enough

*What are some of the differences between multispectral and hyperspectral sensing?

Multispectral: in visible and infrared, works well in homogenous areas

Hyperspectral: uses pushbroom, only in infrared

what platforms and/or sensing systems are associated with digital frame cameras; whiskbroom scanners and pushbroom scanners?

whiskbroom: scan a continuous series of narrow ground strips using a rotating/oscillating mirror. the forward motion of the spacecraft causes new ground strips to be covered by successive scan lines
Landsat sensors

pushbroom: use a linear array of charge-coupled devices (CCDs). There is a dedicated detector element for each ground resolution cell (no scanning mirror) uses the forward motion of the spacecraft to sweep the linear array across the ground scene; each band has own linear array/detector; ot common in wavelength longer than Mid-IR,
linear arrays dwell over longer period of time, high radiometric resolution
no moving parts but still need to calibrate detectors
limited range of spectral sensitivity (up to mid-IR)

SPOT, Hypersectral VNIR

*Be able to provide examples of satellite multispectral remote sensors; airborne/satellite radar sensors; and airborne/satellite hyperspectral sensors

satellite multispectral remote sensors: GOES, land observation satellites

airborne/satellite radar sensors:

satellite hyperspectral sensors:

what is the signal to noise ratio? is it used to describe image or instrument quality?

signal = diffs in image brightness caused by actual variations in scene brightness

noise = variations unrelated to scene brightness due to instrument

need to balance radiometric sensitivity with pixel size, operational altitude, etc. to maintain high SNRs

sun synchronous vs geostationary

sun-sychronous: orbits with respect to earth to match same local sun time each day, x day repeat cycle, inclination with respect to equator in descending mode; typically 9:30-10:30 am local sun time (trade off btwn ideal illumination and min. cloud cover)

geostationary: fixed altitude- 36000km (same period as earth's surface, in equatorial plane, images entire hemispherical disk

what are two atmospheric windows for thermal remote sensing

i think microwave and VIS

3 - 5 μm and 8 - 14 μm

what is the main difference between thermal infrared and near infrared (NIR) / short wave infrared (SWIR)?

thermal infrared is emitted energy, whereas the NIR/SWIR is reflected energy, similar to visible light. thermal sensors are more difficult to calibrate and geo-reference

What is the relationship between "area under the curves" (AUCs) and total spectral radiance?

area under its curve : amount of energy emitted by an object

as temp of an object increases, its dominant wl shifts to shorter wavelengths

apparently they are the same thing?
Wien's displacement law and Stefan Boltzmann law talk about it

Radiant energy peaks of the Earth and the Sun

Sun: 6000K or .483um
Earth: 300K or 9.7 um

*Why is knowing an objects dominant wavelength important to thermal remote sensing?

probably has to do with wien's displacement law or kirchhoffs or emissivity....

What is emissivity of a ratio of? What is the emissivity a measure of?

emissivity = radiant flux eiting a real world selective radiating body (Mo): blackbody at the same temp (Mb); composed of selectively radiating blackbodies- rock, soil, vegetation, water

emissivity is a measure of an object's efficiency as an absorber and emitter of EMR

different emissivities give rise to dramatic variations in radiant flux at diff wavelengths

two objects can have the same temp, but have diff responses to thermal IR image

What does high emissivity (near 1 ) mean?

absorb and radiate high amounts of energy

What does low emissivity (near 0) mean?

absorb and radiate low amounts of energy

What properties influence emissivity values in a thermal infrared image?

color, surface roughness, moisture content, compaction, field of view (spatial res), wavelength, angel of incidence, angel of exitance

What are the emissivity properties of water, ice, soil, and minerals; and ground vegetation?

water, ice, and snow- high emissivity, .94-.99
snow is unusual in that it has a high reflectance in the solar/visible region where most of the downwelling energy is during the day, and a very high emissivity in the thermal region

soil and minerals- exhibit strong spectral features. the dryer, purer soils have lower emissivities in this region

green vegetation; typically has high emissivity because it is structured and contains water. dry/senescent vegetation has a more variable emissivity, esp in 3-5um region, which depends on the type of structure of cover type, dryness, etc.

Does emssivity of a real world object change with wavelegnth?

Different emissivities give rise to dramatic variations in radiant flux at different wavelengths.

Two objects can have the same temperature, but have different responses in a thermal IR image.

real objects: selectively radiating bodies; emissivity varies with wavelengthemissivity is why everything is not a blackbody; it’s a ratio

Examples of thermal RS applications

urban heat islands, snow cover mapping, surface temp mapping, day vs night clouds...

What are thermal crossover times and when is it not wise to do thermal remote sensing?

thermal crossover times: normally occurs 2x daily when the temp conditions are such that there i a loss of contrast between two adjacent objects in infrared imagery

At the thermal crossover times, most of the materials have the almost same radiant temperature, it is not wise to do thermal remote sensing. During early evening, rock & soil still warmer than surrounding terrain

What are LiDAR, Radar, Sonar, TLS, and ALS acronyms

LiDAR: Light Detection and Ranging
ALS: Airborne Laser Scanning (aerial based lidar)
TLS: Terrestrial Laser Scanning (ground-based lidar)
sonar: Sound Navigation and Ranging
Radar: Radio Detection and Ranging
SAR: Synthetc Aperture Radar

What are three advantages of active remote sensing compared to passive?

ability to obtain measurements at anytime, regardless of day or season

examine wavelengths that are not sufficiently provided by sun (such as microwaves)

more control over how the object is illuminated

*Are sonar systems designed to sense signals on the electromagnetic spectrum?

no, because sonar does sound

Be able to describe how LiDAR works

a pulse of light is emitted and the precise time is recorded

the reflection of that pulse is detected and the pricise time is recorded

using the constant speed of light, the delay can be converted into a 'slant range' distance

knowing the position and orientation of the sensor, the XYZ coordinate of the reflective surface can be calculated

How does multiple pulse return theory work

from what i understand, it's like the signal leaves the sensor, heading towards the ground. the moment it hits something, it returns back to sensor, then goes back down again, and returns once it hits another thing. the problem with this is that it can hit a leaf, and it won;t see anything underneath that leaf.

What are some similarities and differences between ALS and TLS

Similarities: active sensors, use light theory, generate point clouds, multiple or single return and full waveform systems

dufferences: laser footprint size: large vs small, orientation (nadir vs horizontal); location (moving vs fixed); spatial reference (IMU vs geo-located targets); point spacing (limited vs a lot); coverage (uniform vs radial); integrated RG cameras on TLS

What are the six components of an active radar system

-pulse generator

slant-range geometry vs ground range geometry

slant range: uncorrected radar imagery; it is based on the actual distance from the radar to each of the respective features in the scene

ground-range: it is possible to convert the slant range display into the true ground-range display on the x axis so tht features in the scene are in their proper planimetric (x,y) position relative to one another in the final radar image

what is polarization

unpolarized energy vibrates in all possible directions perpendicular to the direction f travel.

radar antennas send and receive polarized energy. this means that the pulse of energy is filtered so that its electrical wave vibrations are only in a single plan e that is perpendicular to the direction of travel. the pulse of electromagnetic energy sent out by the antenna may be vertically or horizontally polarized.

polarization is the phenomenon in which waves of light or other radiation are restricted in direction or vibration

important because even if two beams are identical except with diff polarization, they might interact with matter differently

polarization is a property of waves that describes the orientation of their oscillations

a surface that is an ineffective depolorizer scatters NRG in same polarization that was transmitted: appear brighter in HH and darker in HV

*what causes polarization

well, causes of DEpolarization are related to the physical and electrical properties of the ground surface: a rough surface (compared to wavelength) can depolarize the signal a smooth surface might polarize a signal

Brighter in HH, darker in HV

what is the significance of the backscatter coefficient in the radar equation

the radar equation: the fundamental variables influencing the rbightness of a region on a radar image

backscattering coefficient: not controlled by the radar system but by the specific characteristics of the terrain surface represented by a specific region on the image.

the coefficient is the primary focus of study for the image interpreter, as it is this quantity that carries information about the landscape

backscatter coefficient is the quantitative measure of the intensity of energy returned to the radar antenna from a specific area on the surface of the earth

what can't image brightness values be directly interpreted in radar images

so, I'm thinking that this means uncalibrated- image brightness values don't equal backscattering values in the landscape. in radar images, many elements are of varying brightness.

what terrain property most strongly influences the strength of the radar backscatter

surface roughness

why do radar image analysts compare like-polarized images to cross-polarized images?

you can learn about characteristics of terrain surface

like polarized- HH or VV

cross-polorized - VH or HV

a surface that is an effective depolorizer scatters NRG in diff polarization that was transmitted: appear darker in HH and lighter in HV

we discussed in class the relationship between a material's ability to conduct electrical energy, moisture content, and backscattered energy. Be able to describe (pg 311)

a material's electrical conductability is directly related to moisture content- which will therefore hae an impact on the amount of backscattered radar energy.

moist soils reflect more radar energy than dry soils, which absorb more of the radar wave.

soil moisture also influences how deep the radar signal can penetrate into ground surface (the less, the more moist it is)

why are big data and remote sensing complementary?

big data: computers crunch vast quantities of info to identify useful patterns that might otherwise be overlooked

show impact of human decisions on land. basically, look at the really big problems


not forcing that image into a specific number of classes, it’s letting the algorithm naturally run until it puts itself where it needs to be characteristics of training data

decision trees

also called hierarchical classifiers

can use a single image or multiple images for input

not limited to the spectral domain (like slope, soil, aspect, etc)

random forest is based on decision trees and machine learning- currently widely used

kirchhoff's law

good absorbers are good emitters, good reflectors are poor emitters

kinetic heat & temp

kinetic heat: the energy of particles in random motion causes changes in the energy state - resulting in EM radiation emissivity

kinetic temp (true temp): measure of the concentration of kinetic heat

internal kinetic heat is converted to radiant energy

thermal remote sensing measures the flux of exiting radiant energy

radiance vs reflectance

radiance: what is measured directly by RS instruments; what the sensor sees

reflectance: a ratio of the amount of light leaving a target to the amount of light striking the target