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Image Classification
Digital Image Processing Techniques
Image Restoration Image Enhancement Image ClassificationImage Classification
Image Classification: the art and science of using the computer to interpret the image.
Why do it?Why do it?
Especially when automated computer methods oppose a long “proven” history of visual/manualimageinterpretation
However, with image classification you can make cool looking maps with more spatial detail than humans would ever draw!
Coop Project withCal Fish and Game15-meter Landsat7Pan Sharpened ImageryModified CWHRClassification
#Y
#Y#YLocations 6 and 7
Location 4
Post-fire ConditionsBG, bare ground & discolored foliage >70% deadVST, only vertical stems remain >90% deadVSB, vertical stems & branches remain >90% deadSHDB, shadow area, suspect burned 60-70% deadCRNS, only upper third of discolored crowns remain >90% deadBL, consumed lwr crown, upr 2/3 crown discol. >70% deadDIS, discolored crown 40-70% deadSHD, shadow area, no inference possibleSHDH, shadow area, suspect unburned <25% deadUNB, apparently unburned <25% dead vegetationMargin, outside image area
#Y Field Data Locations
1000 0 1000 2000 Feet
Represent detailed conditions on the ground
Forest Cover Classification in Cameroon
Semi-automatedchange detection
A Combination of supervised image A Combination of supervised image classification, polygon formation and classification, polygon formation and visual editing of resulting polygons visual editing of resulting polygons proves useful for forest monitoring.proves useful for forest monitoring.
Semi-AutomatedChangeDetectionBased upon5km by 5kmBlocks ofSatelliteImagery
Image to Image Registration
Accomplished withSPEAR tools
In ENVI
The multi-datestackedimageallowscreationoftwo-datecolorcompositesthat allow the visualidentificationof change
ENVI EX used to classify the image block into four classes:
Forest (unchanged)Non-Forest (unchanged)Deforestation (forest changed to non-forest)Reforestation (non-forest changed to forest)
Training areas defined for all spectral classes visiblein the image
Smooth the image before creating polygons
Area Summary Table
CLASS_NAME area percentageDeforestation 58.30 2.33Reforestation 13.66 0.55Forest 2083.93 83.27Non-forest 346.61 13.85
2502.50 100.00
Objectives:
Understand the principle of supervised Understand the principle of supervised classification including definition of classes classification including definition of classes and selection of training areasand selection of training areas
Describe the maximum likelihood Describe the maximum likelihood classification algorithm, the one most often classification algorithm, the one most often used.used.
Image Classification
SupervisedSupervised Training stage - analyst determines Training stage - analyst determines
source identitysource identity Classification stageClassification stage Output stageOutput stage
Supervised Classification
SelectTrainingAreas
Edit/ Evaluate Signatures
EvaluateClassification
Classify Image
Subjective human influence selects “representative samples” of all landcover types required for the analysis< 5% of the pixels used for training.
Subjective human judgment resolvesproblems: spectral signatures not separable, or spectral signaturesredundant.
Unbiased machine determines theclass into which the unknown pixelsare assigned (>95 % of the pixels areunknown before classification).
Again subjective humans evaluateresults and define new classes to change things as they desire.
Spectral response measurements (spectral signatures) recorded across 7 Landsat TM Spectral response measurements (spectral signatures) recorded across 7 Landsat TM bands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIRbands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIR
Classification Based on Spectral Signatures
1 2 3 4 5 6 71 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7WaterSand Forest UrbanCorn Hay
Adapted from Lillesand and Kiefer, 1999
Supervised Classification
C
C
C
C
C
C
C
C
C C
C
U
UU
U
UU
U
F
U
F
F F F
FFFFF
F
F
F
F
F
S S S
S
S
S
S
S
S
F F
F
W
W
W
WW
WW W
W W
W
SS
S
S
S
F
F
F
Water
Sand
Forest
Urban
Corn
Hay
Classification Stagecompare unknown pixels to known spectral “signatures”
Output Stagetypically, a color-coded map
Training Stagecreate classes
6
3
2
5
4
2
2
3
2 3
2
2
53
1
45
3
3
2 3 1 2
21524
6
3
4
5
5
5 2 3
6
5
3
4
2
2
3 2
4
1
5
3
44
32 3
3 3
6
25
4
6
5
3
1
2
Adapted from Lillesand and Kiefer, 1999
identify training areas of uniform class land cover
assign to most similar
Traditional way to view spectral signatures.
BAND 3 BAND 4
redvisible
very near IR
waterforest
haycorn
urban sand
U U
U
U
U
U
UU
U
U
U
UU
U
U
U
U
U
UU UU
U
U
U U
U U
UU
S
SSSS
SS
S
CCCCC
CC
CCCCC
HHHHHH HH
HHHH
H
HHH
H
HH
HHHHHH
HH F F
FF
FFF
FF
FFF
F FF
FFF
FF
FF
FFFW
WWW
W WW
W WWW
WW
Band 3
Dig
ital N
um
ber
Band 4 Digital Number
Supervised Classification Stage
Two-band scatter Two-band scatter diagram showing diagram showing spectral separability spectral separability of different land of different land covers covers
water
urban
hay
sand
corn
forest
1
2
3
Determine land cover class of each pixel in the scene
Adapted from Lillesand and Kiefer
U U
U
U
U
U
UU
U
U
U
UU
U
U
U
U
U
UU UU
U
U
U U
U U
UU
S
SSSS
SS
S
CCCCC
CC
CCCCC
HHHHHH HH
HHHH
H
HHH
H
HH
HHHHHH
HH F F
FF
FFF
FF
FFF
F FF
FFF
FF
FF
FFFW
WWW
W WW
W WWW
WW
Band 3
Dig
ital N
um
ber
Band 4 Digital Number
Supervised Classification -Maximum Likelihood Classifier
Gaussian probability Gaussian probability function computed function computed for each pixel for for each pixel for each classeach class
1
2
3
Adapted from Lillesand and Kiefer
Pixel assigned to class for which its probability of membership is the greatest.
Can be limited to some number of standard deviations or probability threshold.
Classification - Error MatrixPixels as classified by
ground truthPix
els
as
class
ified b
yth
e c
om
pu
ter
Cla
ssifi
cati
on a
ccura
cy f
rom
use
r’s
vie
wif c
om
pute
r cl
ass
ified a
pix
el as
urb
an,
how
acc
ura
te w
as
that
class
ifica
tion?
629
/689=
91
%…
9%
Err
or
of
Com
mis
sion
Classification accuracy from producer’s view…how many of the known urban pixels were
classified by the computer as urban?629/702=90%…10% Error of Omission
Correctly classified pixels
Overall Accuracy =1033+629+385
+319+20/2578 = 93%