Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery
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Transcript of Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery
![Page 1: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/1.jpg)
Relationships between Land Cover and Spatial Statistical Compression
in High-Resolution Imagery
James A. Shine1 and Daniel B. Carr2
34th Symposium on the Interface19 April 2002
1 George Mason University & US Army Topographic Engineering Center2 George Mason University
![Page 2: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/2.jpg)
Outline of Talk
•The Variogram• Motivation and Procedure• Past Results• Present Results• Analysis and Conclusions• Future Work
![Page 3: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/3.jpg)
Spatial Statistics: The Variogram
-A plot of average variance between points
vs. distance between those points (L2)
-If data are spatially uncorrelated, get a straight line
-If data are spatially correlated, variance generally increases with distance
-Directional component also a consideration (N-S, E-W, omnidirectional)
![Page 4: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/4.jpg)
0 10 20 30 40distance
0
20
40
60
80
100
120
140
gam
ma
Typical image variogram (left),
Important quantities (right)
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Some graphs of variogram models
NUGGET MODEL
h
gam
ma
0 5 10 15 20 25 30
0.8
0.9
1.0
1.1
1.2
LINEAR MODEL
h
gam
ma
0 5 10 15 20 25 30
05
1015
2025
30
SPHERICAL MODEL
h
gam
ma
0 5 10 15 20 25 30
0.2
0.4
0.6
0.8
1.0
EXPONENTIAL MODEL
h
gam
ma
0 5 10 15 20 25 30
0.2
0.4
0.6
0.8
1.0
![Page 6: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/6.jpg)
A double or nested variogram
DOUBLE EXPONENTIAL MODEL
distance
ga
mm
a
0 5 10 15 20 25 30
0.5
1.0
1.5
2.0
+
+
++
++ + + + + + + + + + + + + + + + + + + + + + + + +
o
oo
oo
oo
o o o o o o o o o o o o o o o o o o o o o o o
X
X
X
X
X
XX
XX
XX X X X X X X X X X X X X X X X X X X X
![Page 7: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/7.jpg)
Variogram Applications
-Determination of range for sampling applications:
ground truth
supervised classification
-Model for estimation/prediction applications (forms of kriging)
![Page 8: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/8.jpg)
Outline of Talk
• The Variogram
•Motivation and Procedure• Past Results• Present Results• Analysis and Conclusions• Future Work
![Page 9: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/9.jpg)
MOTIVATION
Large data sets, computational challenges
(10^6-10^7 data points per km^2 at 1 m resolution for pixels)
Large computation times not conducive to real-world applications such as rapid mapping
Compression will reduce computation time,
But how much can we reduce without losing information?
![Page 10: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/10.jpg)
PROCEDURE
Transfer data from imagery to text file
Compute variograms (FORTRAN code)
Format and plot the variograms
Compare variograms with full data sets vs variograms with reduced data sets
![Page 11: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/11.jpg)
Imagery
Ft. A.P. Hill, Ft. Story (both in Virginia) : 1-meter resolution, 4-band CAMIS imagery, collected by US Army Topographic Engineering Center (TEC)
Others: 4-meter resolution, 4-band IKONOS imagery, obtained from TEC’s imagery library and also commercially available.
Bands:
1. Blue (~450 nm)
2. Green (~550 nm)
3. Red (~650 nm)
4. Near Infrared (~850 nm)
![Page 12: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/12.jpg)
Outline of Talk
• The Variogram• Motivation and Procedure
•Past Results• Present Results• Analysis and Conclusions• Future Work
![Page 13: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/13.jpg)
Previous Results: Ft. A.P. Hill, VA (Shine, Interface 2001)
Mostly forest, some manmade
2196 x 2016=4.4x10^6 pixels
![Page 14: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/14.jpg)
Compression works well for AP Hill imagery; Band 1 (blue) variograms shown below
![Page 15: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/15.jpg)
Other A.P. Hill bands also compressed well: Band 2 (Green), N-S at right,
E-W bottom left,
Average bottom right
![Page 16: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/16.jpg)
Band 3 (Red), N-S at right,
E-W bottom left,
Average bottom right
![Page 17: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/17.jpg)
Band 4 (IR), N-S at right,
E-W bottom left,
Average bottom right
![Page 18: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/18.jpg)
Outline of Talk
• The Variogram• Motivation and Procedure• Past Results
•Present Results• Analysis and Conclusions• Future Work
![Page 19: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/19.jpg)
Fort Story, VA results completed,
Plus some new imagery:
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 20: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/20.jpg)
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 21: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/21.jpg)
Original Ft. Story image:
Water, forest, urban
3999x4999=
2.0x10^7 pixels
![Page 22: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/22.jpg)
Ft. Story,original
Band One (Blue)
N-S at right,
E-W bottom left,
Average bottom right
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Ft. Story,original
Band Two(Green)
N-S at right,
E-W bottom left
![Page 24: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/24.jpg)
Ft. Story Results
-Full variogram is very smooth (exponential/spherical), but compression is not good; compressed variogram significantly different from full variogram
-Why does AP Hill compress well and Story does not? Could be losing a level on a nested model (right), but perhaps different landcover or terrain reacts differently to compression.
-Need to compare different types of imagery and hopefully make some inferences
DOUBLE EXPONENTIAL MODEL
distance
ga
mm
a
0 5 10 15 20 25 30
0.5
1.0
1.5
2.0
+
+
++
++ + + + + + + + + + + + + + + + + + + + + + + + +
o
oo
oo
oo
o o o o o o o o o o o o o o o o o o o o o o o
X
X
X
X
X
XX
XX
XX X X X X X X X X X X X X X X X X X X X
![Page 25: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/25.jpg)
Subarea from Ft. Story:
just forest
524x408=2.1x10^5 pixels
![Page 26: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/26.jpg)
Ft. Story forest subimage
Band One (Blue)
N-S at right,
E-W bottom left
Average bottom right
![Page 27: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/27.jpg)
Ft. Story forest subimage results
-Variograms seem to be unbounded (linear)
-Compression matches original pretty well, much better than for the full image
-Do some more tests with other images and landcovers
![Page 28: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/28.jpg)
New Results:
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 29: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/29.jpg)
New York City
2000 x 2000
Urban, water, smoke (9/12/01)
![Page 30: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/30.jpg)
New York City
Blue
E-W,
N-S, average
![Page 31: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/31.jpg)
New York City
Green
E-W,
N-S, average
![Page 32: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/32.jpg)
New York City Results
-Variogram seems unbounded (linear)
-Almost no difference between the full and compressed variograms
![Page 33: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/33.jpg)
New Results:
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 34: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/34.jpg)
Fort Stewart
Mostly fields
2559x2559=
6.5x10^6 pixels
![Page 35: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/35.jpg)
Ft. Stewart
Blue
E-W,
N-S, average
![Page 36: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/36.jpg)
Ft. Stewart
Green
E-W,
N-S, average
![Page 37: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/37.jpg)
Ft. Stewart
Red
E-W,
N-S, average
![Page 38: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/38.jpg)
Ft. Stewart
IR
E-W,
N-S, average
![Page 39: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/39.jpg)
Ft. Stewart Results
-Full variogram is very smooth (exponential/spherical)
-Almost no difference between full and compressed variograms, except very slightly in Blue band
![Page 40: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/40.jpg)
New Results:
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 41: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/41.jpg)
Ft. Moody fields1202x1742=2.1x10^6 pixels
![Page 42: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/42.jpg)
Ft. Moody fields
Blue
E-W,
N-S, average
![Page 43: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/43.jpg)
Ft. Moody fields
Green
E-W,
N-S, average
![Page 44: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/44.jpg)
Ft. Moody fields
Red
E-W,
N-S, average
![Page 45: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/45.jpg)
Ft. Moody fields
IR
E-W,
N-S, average
![Page 46: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/46.jpg)
Ft. Moody forest1325x1767=2.3x10^6 pixels
![Page 47: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/47.jpg)
Ft. Moody forest , Blue , E-W
(no spatial dependence after 3 pixels, so compression is useless; all bands and directions give same non-dependence)
![Page 48: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/48.jpg)
Ft. Moody Results-Field subset variogram is mixed: mostly linear in visible bands, mostly spherical/exponential in IR band. Compresses well although compressed variogram is greater in magnitude than full variogram for the Blue and Green bands
-Forest subset shows no spatial dependence, compression is irrelevant
![Page 49: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/49.jpg)
New Results:
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 50: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/50.jpg)
Wright-Patterson AFB, Ohio
mostly fields, some urban
1385x1692=2.3x10^6 pixels
![Page 51: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/51.jpg)
Wright-Patterson Blue
E-W,
N-S, average
![Page 52: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/52.jpg)
Wright-Patterson Green
E-W,
N-S, average
![Page 53: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/53.jpg)
Wright-Patterson Red
E-W,
N-S, average
![Page 54: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/54.jpg)
Wright-Patterson IR
E-W,
N-S, average
![Page 55: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/55.jpg)
Wright-Patterson Results-A slight loss of variogram with compression, especially in blue and green
-Spherical/exponential variogram
![Page 56: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/56.jpg)
New Results:
Fort Story, VA
New York City
Ft. Stewart, GA
Ft. Moody, GA
Wright-Patterson AFB, OH
Ft. Huachuca, AZ
![Page 57: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/57.jpg)
Ft. Huachuca, AZarid desert and mountains with dry drainage patterns2551x1806=4.6x10^6 pixels
![Page 58: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/58.jpg)
Ft. Huachuca
Blue
E-W,
N-S, average
![Page 59: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/59.jpg)
Ft. Huachuca
Green
E-W,
N-S, average
![Page 60: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/60.jpg)
Ft. Huachuca
Red
E-W,
N-S, average
![Page 61: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/61.jpg)
Ft. Huachuca
IR
E-W,
N-S, average
![Page 62: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/62.jpg)
Huachuca Results-Almost no loss of variogram with compression .
-Variogram is smooth (spherical/exponential)
![Page 63: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/63.jpg)
Computing Benchmarks-Plots of overall execution time versus total number of pixels to be processed:
without Ft. Story full with Ft. Story full
![Page 64: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/64.jpg)
Ratio of computation time (full/reduced) increases as pixel size increases
![Page 65: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/65.jpg)
Outline of Talk
• The Variogram• Motivation and Procedure• Past Results• Present Results
•Analysis and Conclusions• Future Work
![Page 66: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/66.jpg)
Most losses occurred in the Blue and Green bands; Red and IR seem to compress better. Checkered fields in particular showed a slight loss in compression for Blue and Green (Wright-Patterson and Ft. Stewart)
Most land cover types show a spherical/exponential type of variogram. The exceptions seem to be pure forest (linear or no spatial variation) and pure urban (linear)
Mixtures in particular seem to show a spherical/exponential type of variogram.
![Page 67: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/67.jpg)
Still no definitive answer to the major loss of spatial information for full Ft. Story image. Best theory: have lost a level of variation in a nested spherical or exponential model (low-level scale <= 20 meters).
Overall, spatial statistical compression works well for a wide variety of land cover types; may lose some information, but the range is pretty constant, and the gain in computation is immense. (Be careful with forests, though – further tests definitely needed there).
![Page 68: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/68.jpg)
Outline of Talk
• The Variogram• Motivation and Procedure• Past Results• Present Results• Analysis and Conclusions
•Future Work
![Page 69: Relationships between Land Cover and Spatial Statistical Compression in High-Resolution Imagery](https://reader036.fdocuments.in/reader036/viewer/2022062408/5681339c550346895d9aae04/html5/thumbnails/69.jpg)
Future Work
• Compare random,average compression with systematic compression
• Test for further compression (64X) with 1 m imagery
• Improve software code and streamline implementation
• Parallelize variogram computations• Improve graphs