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Use of Use of AerialAerial Videography in Habitat Survey Videography in Habitat Survey
and Computers as Observersand Computers as Observers Leonard Pearlstine University of Florida
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Land Cover Classification
Landsat TMLandsat TM Digital CameraDigital Camera
Landsat TM Schinus Signature
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1 21 41 61 81 101
121
141
161
181
201
221
241
Reflectance Value
Re
lati
ve
Fre
qu
en
cy
TM Band 4
TM Band 2
TM Band 3
Digital Camera Schinus Signature
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0
21
42
63
84
10
5
12
6
14
7
16
8
18
9
21
0
23
1
25
2
Reflectance Value
Re
lati
ve
Fre
qu
en
cy
Layer 1
Layer 2
Layer 3
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Texture
The spatial (statistical) distribution of gray tones.The spatial (statistical) distribution of gray tones.-Haralick et al. 1973
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Desirable Texture Characteristics
• Angularly independent
•Invariant under gray level transformations
•Simple algorithms
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Brazilian Pepper
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Measures of Edge
DensityDensity MagnitudeMagnitude
Rate of ChangeRate of Change
““Visual discrimination of pattern is based primarily on clusters or Visual discrimination of pattern is based primarily on clusters or lines formed by proximate points of uniform brightness” -Julesz 1962lines formed by proximate points of uniform brightness” -Julesz 1962
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Edge Signatures
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Edge Signatures
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Multivariate Discrimination
Logistic Regression selected for
• Heteroscedastic Variances• Dichotomous Classification
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Reference
Classified
Producer’s Accuracy
34% 71% 61%
User’s Accuracy 98% 97% 80%
Logistic Regression
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Commission Error 16% 21% 19%
Reference
Classified
Logistic Regression – No Schinus Images
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Omnidirectional Variogram
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Compute Homogeneity Index Image
Pasture
Trees canopy
Grass
Individual Trees
0
1000
2000
3000
4000
5000
6000
7000
1 9 17 25 33 41 49 57 65 73 81
grass
Indiv. Trees
pasture
Trees
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Edge Textures Application Interface
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Birds Detection and Counting
Video Still Image showing Birds Colony of approximately 150 birds
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Template Matching
•Identify Bird Template(s)
•Area Based Matching (e.g. Correlation Matching)
9x9 bird Template
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Area Based Matching(correlation Matching)
•Compute The correlation Coefficient between Template and Reference Image as:
R(x,y) = ΣΣ(T’(x’,y’)*I’(x’+x,y’+y))
Where:
T~(x,y) = T(x,y) – T & I~ (x+x’,y+y’) = I(x+x’, y+ y’) – I
T and I are the mean under the Template and reference windows respectively.
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Correlation Image
•Bright values indicates Template and Reference images match and Birds Existence
Correlation Image
Reference Image
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Threshold and Identify Birds
Different Threshold can be used.•High Threshold Missing Birds (Increase Omission errors).
•Low Threshold Add noise and other features as Birds (Increase Commission errors).
Threshold = 140Birds Count = 153Actual Birds = 150
Missing Birds
No Birds
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Progressive Scan Video Image
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Progressive Scan Video Image with Bird Pattern Matching
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Birds Count Application Interface
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Conclusions
•Characterizations of edge can effectively discriminate vegetation classes.
• Multivariate discrimination using logistic regression substantially improved accuracies.
• The logit model successfully identified Schinus terebinthifolius and excluded most other vegetation types.
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Conclusions
• Additional work needs to be done to separate Sabal palmetto signatures from Schinus.
• “Big white birds” can be effectively discriminated in even low quality videography.
• Larger sample sizes over a greater geographic extent and with additional species will be needed before these procedures can be considered operational.
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Conclusions
• The modeling approach develop in this dissertation provides an effective procedure for rapid and consistent identification of target species from aerial imagery.