problems
DESCRIPTION
Problems. Snow/ice & clouds are both bright Clouds often made of ice Clouds can be warmer or colder than snow/ice surface Visual inspection requires experience to discriminate clouds from ice sheet Thin clouds can be problematic But thin clouds don’t obscure surface ACCA Two pass: - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/1.jpg)
![Page 2: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/2.jpg)
Problems
• Snow/ice & clouds are both bright• Clouds often made of ice• Clouds can be warmer or colder than snow/ice surface• Visual inspection requires experience to discriminate
clouds from ice sheet• Thin clouds can be problematic
– But thin clouds don’t obscure surface• ACCA
– Two pass: • Pass #1: only 1 of 8 filters (NDSI) meaningful for ice/snow• Pass #2: thermal channel analysis is ambiguous over ice/snow
![Page 3: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/3.jpg)
Some good news• Sun is rarely directly overhead• Thin clouds are less problematic– Surface not obscured
• ACCA contains 1 (of 8) filters that has value– Normalized Difference Snow Index NDSI =
band 2 + band 5 band 2 – band 5
Similar band 2 reflectance, but older snow darker than clouds in band 5
![Page 4: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/4.jpg)
Our Approach• Separating bright things from dark things is
easyShadows & rocks & ocean
vs. clouds & ice/snow
• Everything has an edge• Shadow edges match cloud edges• Especially when sun direction known• Also get cloud elevation above ground
![Page 5: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/5.jpg)
Phase 1: Classifications
• Use NDSI (initial threshold of 0.7)– defines binary image of “possible cloud” &”not cloud”
• Simplify morphology of “possible cloud” regions – fill small holes and closing narrow gaps– Applies numeric tag to each “possible cloud” region
• Edge detection of “possible cloud” regions– Thinned to 1-pixel width
• Detect cloud shadows and water– Cloud shadows are brighter than ocean and darker than either snow
or clouds– Bands 3 and 4 area used with thresholds dependent on sun elevation– 2 classes identified
• “possible cloud shadow”• Water
![Page 6: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/6.jpg)
Phase 2: Matching and Scoring
• Uses:– edges of possible clouds (1-pixel wide)– Pixels of possible cloud shadows– Pixels of water
• Move edges “down-sun”• When an edge meets a shadow or water pixel
a ratio is calculated:Ratio = # edge pixels that coincide with a shadow
pixel / # of all edge pixels
![Page 7: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/7.jpg)
Phase 3: Ratio interpretation• Ideal ratio values:
= 1.0, if cloud doesn’t overlap shadow= 0.5, if cloud overlaps shadow
• Empirically set thresholds based on 8-image data set:– Shadow ratio > 0.2 possible cloud is cloud– Water ratio > 0.25 possible cloud is cloud– Image edge ratio > 0.2 possible cloud is cloud
• Results is an image separated into 5 classes:– Cloud– Water– Detected shadows– Rejected clouds– Snow (ice-sheet)
![Page 8: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/8.jpg)
Identified clouds (light gray), detected shadows (dark gray), detected water pixels (grid) and the rejected non-cloud pixels (black).
Identified clouds (light blue), detected shadows (red), rejected non-cloud pixels (dark blue). And ice sheet (black)
![Page 9: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/9.jpg)
NDSI threshold of 0.7 doesn’t always work
• Best values ranged from 0.56 to 0.79• Iteration of threshold value introduced along with “cloud
score” to measure success of cloud detection for each iteration
• Cloud Score = S1 *Sratio – 0.5 * RS1 = # edge pixels matching cloud shadow
Sratio = S1 / total # of edge pixels
R = # edge pixels not matching cloud shadow Sratio biased the results away from large NDSI thresholds that
detected more possible cloud regions and greatly increased the number of edge pixels
![Page 10: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/10.jpg)
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85
NDSI threshold
No
rmalized
Clo
ud
Sco
re
227,117
34,119
12,115
7,121
229,118
229,119
53,115
29,117
![Page 11: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/11.jpg)
We are wiser now
• LIMA taught us that snow is not a diffuse reflector at low sun elevations and a better reflectance model is required
(Bindschadler et al., 2008)
![Page 12: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/12.jpg)
Image sub-sampling to reduce run times
0 5000000 10000000 15000000 20000000 25000000 30000000 35000000 40000000 45000000 50000000
0
100
200
300
400
500
600
700
800
227,117
34,119
12,115
7,121
229,118
229,119
53,115
29,117
# of pixels
Min
utes
Millions of pixels
![Page 13: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/13.jpg)
Summary
• Use of cloud shadows to detect clouds over snow and ice works
• NDSI is a useful pre-detection step and can probably be improved by correct conversion of snow radiance to reflectance
• Use of reference images may be more effective for ice sheets.
![Page 14: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/14.jpg)
![Page 15: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/15.jpg)
![Page 16: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/16.jpg)
The results of Automated NDSI threshold decision for LANDSAT 7 7/121(path/row), 29/117, 229/119, 229/118
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
0.6 0.65 0.7 0.75 0.8 0.85
NDSI threshold
No
rmal
ized
Clo
ud
Sco
re
p7r121.img
p29r117.img
p229r119.img
p229r118.img
The results of Shadow / Cloud Ratiofor LANDSAT 7 7/121(path/row), 29/117, 229/119, 229/118
0
0.5
1
1.5
2
2.5
0.6 0.65 0.7 0.75 0.8 0.85
NDSI threshold
No
rmal
ized
Clo
ud
Sco
re
p7r121.img
p29r117.img
p229r119.img
p229r118.img
![Page 17: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/17.jpg)
Procedures for Automated NDSI threshold decision
Start CDSE with initial NDSI
threshold (0.6)
Calculate the cloud detection score for each cloud cluster during
CDSE proceduresCloud Score = S1*S_Ratio – 0.5*(# of
rejected cloud edge pixels)
Calculate Total_Score adding the cloud
scores for all cloud clusters
Find the maximum score and decide NDSI
threshold with the maximum score
Increase the NDSI threshold by 0.01
• The cloud detection score will decrease
1. When the detected cloud clusters grow too much due to too high NDSI value. (S_Ratio )
2. When the image has more rejected cloud clusters.
• The purpose of multiplying S1 (S1*S-Ratio) is giving a weight of the cloud cluster size to avoid that the image having small cloud clusters has higher score.
• The score value could be any number and relative.
Stop the iteration when the shadow-cloud ratio (total shadow pixels/total cloud pixels) is less than 0.15
![Page 18: Problems](https://reader036.vdocuments.site/reader036/viewer/2022081520/568148e0550346895db5f945/html5/thumbnails/18.jpg)
Cloud Detection using Shadow Matching (CDSE)Cloud detection using
NDSI threshold – cloud mask image
Cloud shadow detection using
band 3, 4, 5
Detect cloud clusters - Label region
algorithm
Cloud edge detection – Morphological
operations
Search for directional cloud shadow existence for
each cloud edge cluster
Calculate a shadow ratio (S_Ratio) = S1/(total # of
cloud edge pixels)S1= # of cloud edge pixels having
detected shadow