Purdue CE 203 Final Exam

Remote Sensing

Sampling electromagnetic radiation to acquire/interpret geospatial data

UAS

Unmanned Aerial System (Drones)

Temporal Resolution

references Frequency of Return to a location, time based

Spectral Resolution

Detail of data representation (number of bands, range of bands)

Spatial Resolution

Area represented by each cell

Radiometric Resolution

Dynamic range of data

Sun - Synchronous Orbit

An orbit set up to cross a latitude the same time every day, keeps same solar illumination of area

What happens when dynamic range increases?

The image has more detail

432 Band?

Inverse colors

321 Band?

True colors

Why would you increase band number in remote sensing?

To track different types of data

C = ?

v(lambda)

Q = ?

hv , hc/lambda

Irradiance

The amount of radiant flux incident upon a surface per unit area of that surface.

Radiant Exitance

Radiant flux leaving a surface per unit area of that surface

Omega = ?

A/r^2

BRDF?

Bidirectional Reflectance Distribution Function

p + a + t = ?

1

Radiometric Adjustment

adjustment done by the user

Geometric Adjustment

adjustments done by software

Name some geometric distortions.

Earth's rotation, curvature, panoramic distortion, relief distortion

What is the effective sideways skew of Earth during frame acquisition?

6 %

Radiometric Enhancement

Makes an image more "viewable" to the human eye

Thresholding Enhancement

Original values are thresholded over selected ranges and assigned to a small number of integer values

Linear Enhancement

Original values are all altered the same way, retains spot in histogram

Piecewise Linear Enhancement

Different transformations are applied over different ranges of the data set

Logarithmic Enhancement

Exponential functions applied to data, more modification at upper/lower ends of range

Histogram Equalize Enhancement

Creates output histogram "equivalent" to uniform distribution

Gaussian Enhancement

Output has Gaussian distribution

Histogram Matching Enhancement

Used to match contrasts over multiple images

Spectral Transformation

Used to differentiate between two bands, mostly used for NDVI band

NDVI

Normalized Difference Vegetation Index

NDVI = ?

(NIR-Red)/(NIR+Red)

High Pass Filter

Enhances image edges

Low Pass Filter

Reduces image noise

Data Classification

Organizes data into thematic classes

Unsupervised Classification

Mostly done by software, specify number of classes and the computer does the rest

Supervised Classification

Mostly done by user, hand draw what features you want in each class then software throws remaining pixels into best fitting class

ISODATA

Iterative - Self Organizing Data, type of Unsupervised Classification

Training Data

Sets a baseline cell value for each class, done in Supervised

Maximum Likelihood Classification

Weights distance when determining class types

LiDAR

Remote imaging method that users laser light, often used by satellites.

Range of Target Determination?

How long it takes for laser pulses to return

DEM

Digital Elevation Model

Why LiDAR?

Can determine many different data types very effectively, such as making DEM's, Coastline Maps, Topography, Vegetation

SLAR

Side-Looking Airborne Radar

SAR

Synthetic Aperture Radar

Why Microwave?

Operates in all weather conditions

Types of Radar Relief Displacement

Foreshortening, Layover, Shadowing

Cross-Polarized Imagery

Alternating V and H configurations (Ex, VH or HV)

Like-Polarized Imagery

Keeping same configurations (Ex, HH or VV)

GIS

Geographic Information System

Data Model

Computerized Reality, in our case

Raster Data Model

Data model representing reality as cells w/ values

Vector Data Model

Has objects/features

Raster Database

Has many co-registered raster layers for different uses

TIN

Triangulated Irregular Network

Why TIN?

Speeds up rendering time

scale = ?

map distance / ground distance

Small Scale

1:250k - 1:100k

Medium Scale

1:50k - 1:25k

Large Scale

1:10k - 1:1k

Thematic Map

Shows a particular theme, (Ex - Choropleth Map)

H (orthometric height) = ? (important)

h (ellipsoid height) - N (geoid height)

Geographic Coordinates

(lambda , phi) - (lat, long)

Vertical Datums?

NGVD29

NAVD88

Horizontal Datums?

NAD27

NAD83

WGS84 - becoming new standard

Projected Coordinates

(x,y) from (lambda, phi)

Mercator Projection

Distortions away from Equator

Transverse Mercator Projection

Distortions away from Prime Meridian

UTM Projection

60 6degree zones, minimal distortion within zone, 1/1000 accuracy

State Plane Coordinate System

110 zones across U.S, 1/10000 accuracy

Co-register

Way to put different files w/ different coordinate systems on same map

Geocoding

Transforming addresses into usable coordinates

Reverse Geocoding

Transforming coordinates into addresses

DOQ

Digital Orthophoto Quadrangle - ortho corrected image with map projection

DRG

Digital Raster Graphics - scanned paper topographic map

DLG

Digital Line Graphs - digitized topographic features

Tobler's First Law of Geography

Everything is related to everything else, but near things are more related than distant things

Local Raster Operations

Doesnt use surrounding cells

Focal Raster Operations

Uses adjacent cells

Zonal Raster Operations

Uses cells in zone - ones with same value

Global Raster Operations

Uses all cells in raster

Quantile Data Set

Each class has same number of data values

Natural Break Data Set

Grouping based on similarity

Equal Interval Data Set

Range for each class is the same

Vertical Axis Shift of UTM Zone

500km west

Raster Data best represents?

Terrain

Difference between NAD83 and WGS84?

1 meter

Conformal

Preserves angle when projected

Vertical Datum reference surface?

Geoid

How many satellites are needed to observe a GPS point?

4

Geoid is perpendicular to what?

Gravity vector

Geoid approximates what?

mean sea level