The document describes several types of satellite imagery including SPOT, Landsat, and IKONOS. SPOT satellites provide panchromatic and multispectral imagery at 10m and 20m resolution respectively. Landsat satellites have provided MSS and TM sensors, with TM offering improved resolution and additional bands. Landsat 7 uses an ETM+ sensor with bands from 15-60m resolution. IKONOS provides very high resolution 1m panchromatic and 4m multispectral imagery.

1. All materials by Austin Troy © 2003
Lecture 12: More Remote Sensing
1. Major Types of Satellite Imagery
2. Remote Sensing Image
Interpretation and Classification
By Austin Troy © 2003
University of Vermont
------Using GIS--
Introduction to GIS

2. All materials by Austin Troy © 2003
Part 1:
Major types of satellite imagery
------Using GIS--
Introduction to GIS

3. All materials by Austin Troy © 2003
Major satellite imagery products
•SPOT
•Landsat TM
•Landsat MSS
•IKONOS
Introduction to GIS

4. All materials by Austin Troy © 2003
SPOT
•Launched by France
• Stands for Satellite Pour
l'Observation de la Terre
•Operated by the French Space
Agency, Centre National d'Etudes
Spatiales (CNES).
Introduction to GIS

5. All materials by Austin Troy © 2003
SPOT
•SPOT 1 launched 1986, decommissioned and the
reactivated in 1997
•SPOT 2 launched 1990, still going
•SPOT 3 launched 1993 and stopped functioning 1996
•SPOT 4 launched in 1998, still going
•SPOT 5 scheduled for April 2002
Introduction to GIS

6. All materials by Austin Troy © 2003
SPOT
•SPOT satellites are in sun-synchronous orbit
•The satellite passes over the same part of the Earth
at roughly the same local time each day
•Its “inclination” is about 8 degrees off of polar orbit
•The fact that the earth is not perfect sphere makes
the orbital plane rotate slowly around the earth (this
would not happen if it were perfectly polar)
Introduction to GIS

7. All materials by Austin Troy © 2003
SPOT
•The slow motion of that orbital plane
matches the latitudinal motion of the
sun in the sky over the year
•Maintains similar sun angles along its
ground trace for all orbits
•That means that the area the sun flies
over always get the same sunlight
angle, which gives constant lighting
Introduction to GIS
Source:http://hdsn.eoc.nasda.go.jp/experience/rm_kiso/sat
ellit_type_orbit_e.html

8. All materials by Austin Troy © 2003
SPOT
Introduction to GIS
Source:http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbo
ok_htmls/chapter6/chapter6.html
This is for
LANDSAT, but
the idea is the
same for SPOT

9. All materials by Austin Troy © 2003
SPOT
•Each SPOT satellite carries two
HRV (high-resolution visible)
sensors, constructed with multilinear
array detectors, or “pushbroom
scanners”, also known as “along
track scanners”
•These record multispectral image
data along a wide swath
Introduction to GIS
Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/spot.htm

10. All materials by Austin Troy © 2003
SPOT
•Pushbroom uses a “linear array” of detectors, so it senses
single column at a time, and uses forward motion to generate
second dimension
•GSD (ground sampled distance), or resolution is set by
sampling interval . Normally results in just-touching square
pixels making up the image
•Each spectral band of sensing requires its own array.
•Pushbroom scanners generally have higher radiometric
resolution because they have longer “dwell time” than across-
track scanners, which move laterally across landscape as also
move forward
Introduction to GIS

11. All materials by Austin Troy © 2003
SPOT
•The position of each HRV unit
can be changed by ground
control to observe a region of
interest that is at an oblique
angle to the satellite—up to
±27º relative to the vertical.
•Off-nadir viewing allows for
acquisition of stereoscopic
imagery (because of the
parallax created) and provides
a shorter revisit interval of 1 to
3 days.
Introduction to GIS
Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/spot.htm

12. All materials by Austin Troy © 2003
SPOT
•Oblique viewing capacity allows it to image any area
within a 900 kilometer swath; can be used to increase the
viewing frequency for a given point during a given cycle.
The frequency varies with latitude: at the equator, a given
area can be imaged 7 times during the same 26-day orbital
cycle. At latitude 45 degrees, a given area can be imaged 11
times during the orbital cycle, i.e. 157 times yearly and an
average of 2.4 days, with an interval ranging from a
maximum of 4 days to a minimum of 1 day.
•Any point on 95% of the earth may be imaged any day by
one of the three satellites.
Introduction to GIS
Source:http://www.spot.com/home/system/introsat/acquisi/
welcome.htm

13. All materials by Austin Troy © 2003
SPOT
•Two modes: panchromatic and multispectral
•Panchromatic: single spectral band, corresponding to the visible
part of the EM spectrum without the blue, from 0.51 to 0.73 µm.
Single channel imaging mode, so yields black and white images.
Resolution is 10 m. Pixels per line is 6000. Good for fine
geometrical detail.
•Multispectral mode: three spectral bands are XS1 covering 0.50
to 0.59 µm (green), XS2 covering 0.61 to 0.68 µ m (red) and XS3
covering 0.79 to 0.89 µm (near infrared). Resolution is 20 m.
Pixels per line is 3000.
Introduction to GIS

14. All materials by Austin Troy © 2003
SPOT
•Some examples: mosaic false color tiles of Australia
Introduction to GIS

15. All materials by Austin Troy © 2003
SPOT
Introduction to GIS

16. All materials by Austin Troy © 2003
SPOT
•SPOT
can be
purchased
online
using a
browser to
select your
area and
product
Introduction to GIS
http://www.spot.com/HOME/PROSER/USASelect_On-
Line/usa_select_on-line.htm

17. All materials by Austin Troy © 2003
LANDSAT
•first started by NASA in
1972 but later turned over
to NOAA
•Since 1984 satellite
operation and data
handling are managed by
a commercial company
EOSAT
Introduction to GIS
Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/landsat.htm

18. All materials by Austin Troy © 2003
LANDSAT
•LANDSAT-1 launched 1972 and lasted until 1978.
•LANDSAT-2 launched 1975
•Three more satellites were launched in 1978, 1982, and
1984 (LANDSAT-3, 4, and 5 respectively).
•LANDSAT-6 was launched on October 1993 but the
satellite failed to obtain orbit.
•LANDSAT-7 launched in 1999
•Only 7 and 5 are still working
Introduction to GIS

19. All materials by Austin Troy © 2003
LANDSAT
•Like SPOT, LANDSAT is sun-synchronous, and is
about 8 degrees off a polar orbit
•Its repeat cycle is about 16 days and always crosses
equator at around 10 AM.
•Orbit takes about 99 minutes (14.5 per day)
•Distance between ground tracks of consecutive orbits
is 2752 km at equator because of the earth’s rotation
•By following earth’s rotation with each pass, it can
keep crossing the equator at the same time
Introduction to GIS

20. All materials by Austin Troy © 2003
LANDSAT
•Swath is 183 km
wide, although that
includes overlap,
since data frame is
170 km
•233 orbits, for each
16 day cycle
Introduction to GIS
Source: http://eosims.cr.usgs.gov:5725/DATASET_DOCS/landsat7_dataset.html

21. All materials by Austin Troy © 2003
LANDSAT
•Scenes are then indexed by the path and a row
Introduction to GIS
Source: http://eosims.cr.usgs.gov:5725/DATASET_DOCS/landsat7_dataset.html

22. All materials by Austin Troy © 2003
LANDSAT
•LANDSAT 4 and 5 had two types of sensors, MSS
(multi-spectral scanner) and TM (thematic mapper):
•MSS:Started on LANDSAT 1, terminated in late
1992. 80 m resolution with four spectral bands from
the visible green to the near-infrared (IR)
wavelengths. Only Landsat 3’s MSS sensor had a
fifth band in the thermal-IR.
Introduction to GIS

23. All materials by Austin Troy © 2003
LANDSAT 4 and 5
MSS:
•TM:
Introduction to GIS
*
*
* Mid infra red

24. All materials by Austin Troy © 2003
LANDSAT MSS
•MSS has a
square
instantaneous
field of view
(IFOV), with
an 11.56 °
field of view.
Introduction to GIS
Source: http://edcwww.cr.usgs.gov/glis/hyper/guide/landsat

25. All materials by Austin Troy © 2003
LANDSAT MSS
•This is a “whiskbroom” rather than “pushbroom”
scanner. AKAAcross track scanning
•Satellite motion provides one axis of the image and
other axis provided for by oscillating mirror
•Has poor radiometric resolution- only 6 bit, or 64
values
Introduction to GIS

26. All materials by Austin Troy © 2003
LANDSAT TM
•Thematic Mapper: more bands, better spatial and
radiometric resolution(256 DNs instead of 64)
•Both resolution improvements, plus the fact that the
green and red bands are narrower make it better for
vegetation discrimination than MSS; also near IR in
TM is narrower and centered in a region that is highly
sensitive to plant vigor.
Introduction to GIS

27. All materials by Austin Troy © 2003
LANDSAT TM: applications
Introduction to GIS
Band Nominal Spectral
location
applications
1 Blue Water body penetration, soil-water discrimination,
forest type mapping, cultural feature ID
2 Green Green reflectance peak of veg, for veg ID and
assessment of vigor, cultural feature ID
3 Red Chlorophyll absorption region, plant species
differentiation, cultural feature ID
4 Near infra red Veg types, vigor and biomass content, dilineating water
bodies, soil moisture assessment
5 mid infra red (1.55-
1.75 mm)
Veg moisture, soil moisture, diff of soil from clouds
6 Thermal infra red Veg stress analysis, soil moisture, thermal mapping
7 mid infra red(2.08-
2.35 mm)
Discriminating mineral and rock types, veg moisture

28. All materials by Austin Troy © 2003
LANDSAT TM
•An example:August 14, 1999 (left) and October 17, 1999 (right)
images of the Salt Lake City area
• differences in color due to growing season
Introduction to GIS

29. All materials by Austin Troy © 2003
LANDSAT 7
•Uses a new sensor called
Enhanced Thematic Mapper
Plus (ETM+)
•Stresses continuity with
LANDSAT 4 and 5 in that
uses similar orbit and repeat
patterns, as well as a similar
185 km swath width for
imaging
•Check out the movie
Introduction to GIS
Source: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter2/chapter2.html
Full info at http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html

30. All materials by Austin Troy © 2003
LANDSAT 7
•Spatial resolution of bands
Introduction to GIS
Source: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter2/chapter2.html
Table 6.1 Image Dimensions for a Landsat 7 0R Product
Band
Number
Resolution
(meters)
Samples
(columns)
Data Lines
(rows)
Bits per
Sample
1-5, 7 30 6,600 6000 8
6 60 3,300 3,000 8
8 15 13,200 12,000 8

31. All materials by Austin Troy © 2003
LANDSAT 7
•Spatial resolution of bands
Introduction to GIS
LANDSAT-7 ETM+ BAND CHARACTERISTICS
Band
Number
Nominal
spectrum
Spectral Range
(µ)
Ground
Resolution
(m)
Data Lines
Per Scan
Data Line
Length (bytes)
1 Blue .450 to .515 30 16 6,600
2 green .525 to .605 30 16 6,600
3 red .630 to .690 30 16 6,600
4 Near IR .775 to .900 30 16 6,600
5 mid IR 1.550 to 1.750 30 16 6,600
6 Thermal IR 10.40 to 12.50 60 8 3,300
7 mid IR 2.090 to 2.35 30 16 6,600
8 panchromatic .520 to .900 15 32 13,200
Band wavelength spectrums are slightly different from LANDSAT 5

32. All materials by Austin Troy © 2003
LANDSAT 7
•LANDSAT 7 has an excellent mission coverage archive
Introduction to GIS
Source: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter6/chapter6.html

33. All materials by Austin Troy © 2003
LANDSAT Products
•All data older than 2 years return to "public domain" and
are distributed by the Earth Resource Observation System
(EROS) Data Center of the US Geological Servey
•Available at
http://edcwww.cr.usgs.gov/products/satellite/landsat7.html
•The LANDSAT Reference system catalogues the world
into 57,784 scenes, each 115 miles (183 kilometers) wide
by 106 miles (170 kilometers) long.
Introduction to GIS

34. All materials by Austin Troy © 2003
Landsat Products:USGS
•OR: no radiometric or geometric correction applied. Scan lines
are reversed and nominally aligned.
•1R: includes radiometric correction, but no geometric correction.
Scan lines are reversed and nominally aligned.
•1G: includes both radiometric and geometric correction. The
scene will be rotated, aligned, and georeferenced to a user-defined
map projection.
Introduction to GIS

35. All materials by Austin Troy © 2003
Landsat Products
•Another source is Space Imaging Inc.
•Note that they have resampled to 15 m
Introduction to GIS

36. All materials by Austin Troy © 2003
LANDSAT Imagery
Introduction to GIS

37. All materials by Austin Troy © 2003
LANDSAT Imagery
Introduction to GIS
Composite of shortwave infrared, Near-Infrared and Red. Shows manmade features as
well as densely forested areas and agricultural lands

38. All materials by Austin Troy © 2003
LANDSAT Imagery
Introduction to GIS
Same bands: shows wetlands, urban, open water, forest

39. All materials by Austin Troy © 2003
LANDSAT Imagery
Introduction to GIS
Same bands: light yellow-green color represents northern hardwood forest. The dark
green patches represent various conifer species

40. All materials by Austin Troy © 2003
IKONOS data
Introduction to GIS
•High resolution satellite developed by Space
Imaging, launched 1999
•Has sun-synchronous orbit and crosses equator
at 10:30 AM
•Ground track repeats every 11 days
•Highly maneuverable: can point at a new target
and stabilize itself in seconds, enabling it to
follow meandering features
•The entire spacecraft moves, not just the
sensors

41. All materials by Austin Troy © 2003
IKONOS data
•Can collect data at angles of up to 45° from the along
track and across track axes
•This allows for side by side and fore and aft
stereoscopic imaging
•At its nadir it has 11 km swath width
•11 km by 11 km image size, but user specified strips
and mosaics can be ordered
•Employs a linear array scanner
Introduction to GIS

42. All materials by Austin Troy © 2003
IKONOS data
•IKONOS collects panchromatic band (.45 to .90 mm)
at 1 m resolution
•Collects four multispectral bands at 4 m resolution
•Bands include blue (.45 to .52 mm) , green (.51 to .60
mm) , red (.63 to .70 mm), near IR (.76 to .85 mm)
•Radiometric resolution is 11 bits, or 2048 values
Introduction to GIS

43. All materials by Austin Troy © 2003
IKONOS data
•Here is 1m IKONOS view of suburbs, near winter Olympics
Introduction to GIS
Source: spaceimaging.com

44. All materials by Austin Troy © 2003
IKONOS data
•1m IKONOS view of Dubai
Introduction to GIS
Source: spaceimaging.com

45. All materials by Austin Troy © 2003
IKONOS data
•1m IKONOS pan image of Rome
Introduction to GIS
Source: spaceimaging.com

46. All materials by Austin Troy © 2003
IKONOS data
•1m image of “Survivor” camp in Africa
Introduction to GIS
Source: spaceimaging.com

47. All materials by Austin Troy © 2003
Part 2:
Remote Sensing Image Processing
and Interpretation
------Using GIS--
Introduction to GIS

48. All materials by Austin Troy © 2003
Image Pre-Processing
•Once an image is acquired it is generally
processed to eliminate errors
•Two categories:
•Geometric correction
•Radiometric correction
Introduction to GIS

49. All materials by Austin Troy © 2003
Geometric Correction
•Sources of distortion
•variations in altitude
•variations in velocity
•earth curvature
•relief displacement
•atmospheric refraction
•Skew distortion from earth’s eastward rotation
Introduction to GIS

50. All materials by Austin Troy © 2003
Geometric Correction
•Raw digital images contain two types of
geometric distortions: systematic and random
•Systematic sources are understood and can
be corrected by applying formulas
•Random distortions, or ‘residual unknown
systematic distortions’ are corrected using
multiple regression of ground control points
that are visible from the image
Introduction to GIS

51. All materials by Austin Troy © 2003
Radiometric Correction
•Radiance measured at a given point is influenced by:
•Changes in illumination
•Atmospheric conditions (haze, clouds)
•Angle of view
•Instrument response characteristics
•elevation of the sun (seasonal change in sun angle)
•Earth-sun distance variation
Introduction to GIS

52. All materials by Austin Troy © 2003
Image enhancement
•For improving image quality, particularly contrast
•Includes a number of methods used for enhancing
subtle radiometric differences so that the eye can
easily perceive them
•Two types: point and local operations
•Point: modify brightness value of a given pixel
independently
•Local: modify pixel brightness based on
neighborhood brightness values
Introduction to GIS

53. All materials by Austin Troy © 2003
Image enhancement
•Three types of manipulation are:
•Contrast enhancement:methods include gray level
thresholding, level slicing and contrast stretching
•Spatial feature manipulation: methods include spatial
filtering, edge enhancement and Fourrier analysis
•Multi-image manipulation: methods include
multispectral band ratioing and differencing, principal
components, canonical components, vegetative
components, decorrelation stretching, others
Introduction to GIS

54. All materials by Austin Troy © 2003
Contrast enhancement
(point operation)
•Most images start with low contrast; these improve it
•Level slicing reclasses DNs into fewer classes, so
differences can be more easily seen; colors or grayscale
values can be assigned. Like resampling down
radiometric resolution. Often used where histogram
shows bimodal distribution of reflectance values
•Contrast Streching is the opposite, where a smaller
number of values are stretched out over full DN range
Introduction to GIS

55. All materials by Austin Troy © 2003
Contrast enhancement
•Here is what spectral histograms look like
Introduction to GIS
Note that DN is not zero for any of them
Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/process.htm

56. All materials by Austin Troy © 2003
Contrast enhancement
Introduction to GIS
Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/process.htm
•The image on the left is hazy because of
atmospheric scattering; the image is
improved (right) through the use of Gray
level thresholding. Note that there is more
contrast and features can be better discerned

57. All materials by Austin Troy © 2003
Spatial Feature Enhancement
(local operation)
•Spatial filtering/ Convolution: neighborhood
operations (like we reviewed for raster analysis), that
calculate a new value for the center pixel based on the
values of its neighbors within a window (see “More
Raster Analysis” lecture for more); includes low-pass
(emphasizes regional spatial trends, demphasizes local
variability ) and high-pass (emphasizes local spatial
variability) filters
Introduction to GIS

58. All materials by Austin Troy © 2003
Spatial Feature Enhancement
•Edge Enhancement: This is a convolution method
that combines elements of both low and high-pass
filtering in a way that accentuates linear and local
contrast features without losing the regional patterns
•First, a high-pass image is made with local detail
•Next, all or some of the gray level of the original
scene is added back
•Finally, the composite image is contrast stretched
Introduction to GIS

59. All materials by Austin Troy © 2003
Image classification
•This is the science of turning RS data into meaningful
categories representing surface conditions or classes
•Spectral pattern recognition procedures classifies a
pixel based on its pattern of radiance measurements in
each band: more common and easy to use
•Spatial pattern recognition classifies a pixel based
on its relationship to surrounding pixels: more
complex and difficult to implement
•Temporal pattern recognition: looks at changes in
pixels over time to assist in feature recognition
Introduction to GIS

60. All materials by Austin Troy © 2003
Spectral Classification
•Two types of classification:
•Supervised: the analyst designates on-screen “training
areas” known land cover type from which an interpretation key
is created, describing the spectral attributes of each cover class .
Statistical techniques are then used to assign pixel data to a
cover class, based on what class its spectral pattern resembles.
•Unsupervised:automated algorithms produce spectral
classes based on natural groupings of multi-band reflectance
values (rather than through designation of training areas), and
the analyst uses references data, such as field measurements,
DOQs or GIS data layers to assign areas to the given classes
Introduction to GIS

61. All materials by Austin Troy © 2003
Spectral Classification
•Unsupervised:
•Computer groups all pixels
according to their spectral
relationships and looks for
natural spectral groupings of
pixels, called spectral classes
•Assumes that data in
different cover class will not
belong to same grouping
•Once created, the analyst
assesses their utility
Introduction to GIS
Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation.
Spectral class 1
Spectral class 2

62. All materials by Austin Troy © 2003
Spectral Classification
•Unsupervised:
•After comparing the reclassified image (based on spectral
classes) to ground reference data, the analyst can determine
which land cover type the spectral class corresponds to
•Has advantage over supervised classification: the “classifier”
identifies the distinct spectral classes, many of which would
not have been apparent in supervised classification and, if
there were many classes, would have been difficult to train all
of them. Not required to make assumptions of what all the
cover classes are before classification.
•Clustering algorithms include: K-means, texture analysis
Introduction to GIS

63. All materials by Austin Troy © 2003
Spectral Classification
•Unsupervised:
•Here’s an
example
Introduction to GIS
Source: http://elwood.la.asu.edu/grsl/lter/fig5.html

64. All materials by Austin Troy © 2003
Spectral Classification
•Unsupervised:Another example
Introduction to GIS
Source: http://mercator.upc.es/nicktutorial/Sect1/nicktutor_1-14.html

65. All materials by Austin Troy © 2003
Spectral Classification
•Supervised:
•Better for cases where validity of classification depends on a
priori knowledge of the technician
•Conventional cover classes are recognized in the scene from prior
knowledge or other GIS/ imagery layers
•Therefore selection of classes is pre-determined and supervised
•Training sites are chosen for each of those classes
•Each training site “class” results in a cloud of points in n
dimensional “measurement space,” representing variability of
different pixels spectral signatures in that class
Introduction to GIS

66. All materials by Austin Troy © 2003
Spectral Classification
•Supervised: Here are a bunch of pre-chosen training sites of
known cover type
Introduction to GIS
Source: http://mercator.upc.es/nicktutorial/Sect1/nicktutor_1-15.html

67. All materials by Austin Troy © 2003
Spectral Classification
•Supervised:
•The next step is for the computer to assign each pixel to the
spectral class is appears to belong to, based on the DN’s of its
constituent bands
• There are numerous algorithms the computer uses, including:
•Minimum distance to means classification (Chain Method)
•Gaussian Maximum likelihood classification
•Parallelpiped classification
Introduction to GIS

68. All materials by Austin Troy © 2003
Spectral Classification
•Supervised:
•These algorithms look at “clouds” of pixels in spectral
“measurement space” from training areas, and try to determine
which “cloud” a given non-training pixel falls in.
•The simplest method is “minimum distance” in which a
theoretical center point of point cloud is plotted, based on mean
values, and an unknown point is assigned to the nearest of these.
That point is then assigned that cover class.
•They get much more complex from there.
Introduction to GIS

69. All materials by Austin Troy © 2003
Spectral Classification
•Supervised:
•Examples of two classifiers
Introduction to GIS
Source: http://mercator.upc.es/nicktutorial/Sect1/nicktutor_1-16.html

70. All materials by Austin Troy © 2003
Classifying Imagery
•Spectral classification can be used for numerous purposes, like
classifying geology, water temperature, soil moisture, other soil
characteristics, water sediment load, water pollution levels, lake
eutrophication, flood damage estimation, groundwater location,
vegetative water stress, vegetative diseases and stresses, crop yields
and health, biomass quantity, net primary productivity, forest
vegetation species composition, forest fragmentation, forest age (in
some cases), rangeland quality and type, urban mapping and
vectorization of manmade structures
•One of the most common applications of classification is land
cover and land use mapping
Introduction to GIS

71. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Land cover refers to the feature present and land use refers to the
human activity associated with a plot of land
•The LU/LC classes to be derived will depend on the system being
used. One of the most common is the USGS Anderson
Classification System (Anderson et al. 1976). This classification
scheme is hierarchical, with nine very general categories at Level I,
and an increasing number of classes and detail and level increases.
Paper available online at http://landcover.usgs.gov/pdf/anderson.pdf
•Anderson system intermixes land use and land cover metrics, by
inferring land use from land cover. Unfortunately, land cover can
only tell us a limited amount about land use—think of outdoor
recreation as a land use. Need additional data for these classes.
Introduction to GIS

72. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Land use and land cover classification system for use with remote
sensor data (Anderson et al. 1976)
•Level I Level II
•1 Urban or Built-up Land 11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications, and Utilities
15 Industrial and Commercial Complexes
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
Introduction to GIS

73. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Level I Level II
•2 Agricultural Land 21 Cropland and Pasture
22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas
23 Confined Feeding Operations
24 Other Agricultural Land
•3 Rangeland 31 Herbaceous Rangeland
32 Shrub and Brush Rangeland
33 Mixed Rangeland
•4 Forest Land 41 Deciduous Forest Land
42 Evergreen Forest Land
43 Mixed Forest Land
•5 Water 51 Streams and Canals
52 Lakes
53 Reservoirs
54 Bays and Estuaries
•6 Wetland 61 Forested Wetland
62 Nonforested Wetland
Introduction to GIS

74. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Level I Level II
•7 Barren Land 71 Dry Salt Flats.
72 Beaches
73 Sandy Areas other than Beaches
74 Bare Exposed Rock
75 Strip Mines Quarries, and Gravel Pits
76 Transitional Areas
77 Mixed Barren Land
•8 Tundra 81 Shrub and Brush Tundra
82 Herbaceous Tundra
83 Bare Ground Tundra
84 Wet Tundra
85 Mixed Tundra
•9 Perennial Snow or Ice 91 Perennial Snowfields
92 Glaciers
Introduction to GIS

75. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Level 3 and 4 categories deliver even more detail.
•USGS only specifies classifications for 1 and 2. They suggest that
higher level classification be designed by local planners who know
the land uses, because of the narrowness of the categories
• As an example for level 3, with “urban” (level 1) “residential”
(level 2) category, includes single family home (111), multifamily
home (112), group quarters (113), mobile home parks (115), etc.
•LANDSAT data can be used to generate level 1 easily, level 2 with
some finesse (15 to 20 m resolution recommended)
•Levels 3 and 4, IKONOS data or aerial photographs are needed.
Level 4 requires much supplemental information
Introduction to GIS

76. All materials by Austin Troy © 2003
Land Cover/ Land Use Mapping
•Here is an example of LANDSAT data classified using the Anderson System
Introduction to GIS

77. All materials by Austin Troy © 2003
Accuracy Assessment
•This is one of the most important parts of image classification.
•Error rates can be very high in classification accuracies, especially
with lower resolution data, and where pixels are mixed
•This is often the most time consuming part of image classification
•NLCD effort undertook effort to classify errors in each type of
land cover, broken down by region of the US
•User’s accuracy for type X: Percent of pixels classified as X that
really are X. Producer’s accuracy: percent of pixels that were
classified as other than X but really are X.
Introduction to GIS

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Object-Oriented Classification
Introduction to GIS
Traditional classifiers don’t work as well for new generation of
high resolution data, like this 2 foot Emerge Color infrared
airphoto. Why? Meaningless to classify each pixel

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Object-Oriented Classification
•This is one of the newer methods of image classification, designed
for high-res data. Looks at the spatial grouping of pixels with
similar reflectance characteristics and can create polygon objects
representing homogeneous areas.
• Allows for complex automated classification rules to be set based
on both the spectral and spatial properties of the data
•Using this information, objects can be classified based on tone,
texture, shape and context
•Allows for nested hierarchical classifications, from general to
highly specific.
Introduction to GIS

80. All materials by Austin Troy © 2003
Object-oriented classification Steps:
•Segmentation of rasters into polygon objects
•Objects are defined such that they minimize within-unit
heterogeneity and maximize between unit heterogeneity, subject to
some user defined parameters.
•The user can control the scale parameter for acceptable level of
heterogeneity. They can also control the degree to which
segmentation is based on spectral or spatial characteristics, since
heterogeneity is defined in terms of both. By repeating the
segmentation with different scale parameters, the user can create a
nested hierarchy of objects>>big objects containing smaller
objects, containing smaller objects
Introduction to GIS

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Object-oriented classification
Introduction to GIS

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Object-oriented classification
Introduction to GIS

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Object-oriented classification Steps:
•Two levels of segmentation
Introduction to GIS
Source/More info: see Ecognition website: http://www.definiens-imaging.com/index.htm

84. All materials by Austin Troy © 2003
Object-oriented classification Steps:
•Following segmentation, each object is encoded with information
about its tone, shape, area, context, neighborhors and spectral
characteristics (e.g. mean, standard deviation, max, min or each
band’s spectral reflectance)
•This information can be used for feature extraction in which
objects’ properties are analyzed to look for characteristics that help
to discriminate one object type from another. That is, what object
information helps discriminate one from another?
Introduction to GIS

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Object-oriented classification
Introduction to GIS

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Object-oriented classification Steps:
•Then objects are classified by either defining training areas of
known cover type (known as supervised fuzzy classification) or
creating class descriptions organized through inheritance-based
rules into a knowledge base (known as fuzzy knowledge base
classification).
Introduction to GIS

87. All materials by Austin Troy © 2003
Object-oriented classification Steps:
•In the knowledge base approach,
complex membership functions can be
derived that describe characteristics that
are typical or atypical for a certain
class. The more a given object displays
the characteristics, the more likely it is
to be classified into the class to which
those characteristics pertain.
Characteristics can be based on spectral
response summary statistics, shape
characteristics, adjacency, connectivity,
and overlay with certain thematic
features.
Introduction to GIS

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Object-oriented classification
Introduction to GIS

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Object-oriented classification Steps
Introduction to GIS
• The classification can be hierarchical and nested, with
finer classifications within coarser ones
• Small classified objects can be aggregated up to large
object classes and large objects can be split into smaller
ones. Can then assign different segmentations to different
class hierarchy level
• Allows for high precision classifications within coarser,
general classifications

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Object-oriented classification Steps
Introduction to GIS
• The classification can be hierarchical and nested, with

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Object-oriented classification Steps
Introduction to GIS
• Can use additional thematic layers to populate the
knowledge base and create rules about what a certain class
can be on top of, next to, or near. This can increase the
accuracy of classifications, especially as you increase
categorical precision and start getting into classifiying
land uses in addition to land cover
• Hence, when you do training areas, you not only get
average spectral responses and shape metrics for a class,
but also can get average values from underlying layers to
help increase classification accuracy
• Examples: farm fields as fn of slope, soils, etc; different
suburban development types as function of distance to
urban centers, income, crime, etc.

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Object-oriented classification Steps
Introduction to GIS
• Error evaluation: Can then
assess the strength of each
classification to see how well
objects fall into them
• However, if a pixel had a high
fit score for two categories, it
is “unstable.” Here are the
mean stability scores
• Stability does down as number
of categories goes up
• This does not tell you
classification, accuracy, which
requires ground truth data

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Object-oriented classification
Software
Introduction to GIS
•eCognition: one of the top Object oriented
classification software packages
More info: see Ecognition website: http://www.definiens-imaging.com/index.htm