glc 2000 ‘final results’ workshop (jrc-ispra, 24 ~ 26 march, 2003) glc 2000 ‘final results’...
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GLC 2000 ‘Final Results’ Workshop(JRC-Ispra, 24 ~ 26 March, 2003)
GLC 2000 ‘Final Results’ Workshop(JRC-Ispra, 24 ~ 26 March, 2003)
LAND COVER MAP OF FRANCE USING S1/S10 SPOT/VEGETATION DATA
LAND COVER MAP OF FRANCE USING S1/S10 SPOT/VEGETATION DATA
Jean-Louis CHAMPEAUXKyung-Soo HAN
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SPOT4-VEGETATION1 DATASPOT4-VEGETATION1 DATA
S1 Daily Synthesis Products– 4 Spectral Channels (b0, b2, b3, SWIR)
– Angular Information (SZA, VZA, SAA, VAA)
Period– 1 January ~ 31 December, 2000
– 351 Daily Synthesis Sets (15 missing days)
Zone
5.50 W ~ 9.91 E
51.50 N ~ 40.01 N
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METHODOLOGY SYNTHESISMETHODOLOGY SYNTHESIS
Spot4-VGT1S1 Daily Synthesis
Cloud Mask
SWIR BlindSuppression
BRDFCorrection
ReflectanceNormalization
Principal ComponentsTransformation
K-mean ClusteringAlgorithm
Map of Unnamed Classes
CORINE Land Cover
ConfusionMatrix
New LandCover MapNew LandCover Map
AngularInformation
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Image Treatment CLOUD MASKCLOUD MASK
New Tuned Thresholdsfor Surface Reflectance
b0 220
SWIR 180
CLOUDY CLEARSKY
Yes
Yes
No
No
b0, 23/08/2000
b0, 5/02/2000
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SWIR BLIND SUPPRESSION-case1SWIR BLIND SUPPRESSION-case1
Image Treatment
Core(defect)
dilatation
Core
–by threshold
Dilatation– Ps(i-1,j) and Pe(i+1,j) Ps(i,j): start pixel of core on a line Pe(i,j): end pixel of core on a line
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SWIR BLIND SUPPRESSION-case2SWIR BLIND SUPPRESSION-case2
Image Treatment
(SWIR-b2)2
= (SWIR-b2)2
=
Threshold = M’ + (M 3.0)M’ : Mean of M values for a lineM : Standard dev of M values for a line
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SEMI-EMPIRICAL BRDF MODELSEMI-EMPIRICAL BRDF MODEL
Image Treatment
s, v, = k0 + k1 f1s, v, + k2 f2s, v, , Roujean et al., 1992
N measured
N: number of clear days during a compositing period
k0: bidirectional reflectance, s = v = 0
Inversio
n
of
system
k0, k1, k2
Computati
on of
normalized
reflectanc
e
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NORMALIZATION OF NORMALIZATION OF
Image Treatment
Method (Duchemin & Maisongrande, 2002):norm,i= model(s=moyen, v=0) + mesured,i model,i(s, v,)
S1-Syn.(RGB)19 June 2002R: SWIRG: b3B: b2
Norm. (RGB)19 June 2002R: SWIRG: b3B: b2
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Day of Year1 10 20 30 40
Composite 1
Composite 2
Composite 3::
Average all norm values for clear day
- 31-day Screening- > 4 Observations
n
iinormN
n 1)(
1
N
NORMALIZATION OF NORMALIZATION OF
Image Treatment
norm(i): norm of day i
N: composite value by N norm
N: number of clear days for the compositing period
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EXAMPLES OF 10-day COMPOSITEEXAMPLES OF 10-day COMPOSITE
Image Treatment
1st 10-day 8th 10-day 12th 10-day
17th 10-day 34th 10-day23 10-day
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23th 10-day ( August)
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TIME SERIES PROFILE TIME SERIES PROFILE
Image Treatment
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AREA OPTIMIZATIONAREA OPTIMIZATION
Memory limitations to run the algorithm over the whole areaMemory limitations to run the algorithm over the whole area
Reduction of running size
Step1.Select a zone through a climate map
Step2.Divide into two parts to avoid
mosaic problems
North part
South part
Classification
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PRINCIPAL COMPONENTSPRINCIPAL COMPONENTS
Classification
North area465,942 pixels26 10-days
(78 input images)
36 10-day images for each channel (=117 variables)
Select images by % of cloudy pixels over each area
South area517,314 pixels
24 10-days(72 input images)
PRINCIPAL COMPONENTS ANALYSIS
Selection of ComponentsComponents having 99% accumulated correlation
North area48 components
South area34 components
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K-MEAN CLUSTERINGK-MEAN CLUSTERING
Classification
North area48 components
South area34 components
K-mean Clustering for 40 classes
1st output (40 classes)
2nd output (44 classes)- 40 classes from K-mean clustering- 4 classes from CORINE
CORINE Mask for misdetected pixels
- Artificial surfaces- Water bodies- Wetlands- Beachs, dunes, sandsCORINE MASK
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Agglomeration of classes fitting
the same landcover
MOSAICMOSAIC
Classification
North partMap
North partMap
South partMap
South partMap
Mosaic
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MISSING
MISSING PIXEL TREATMENTMISSING PIXEL TREATMENT
Classification
Missing due tocloud
Missing due to cloud & snow
Missing due to cloud & snow
K-mean clusteringfor missing pixels
A
B C
With a clear imageafter the end of snow melting
- Julian day 254
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NEW URBAN DETERMINATIONNEW URBAN DETERMINATION
Classification
A Supervised Classification for New Urban Area Class
Toulouse Toulouse New Urban
Artificial Surfaces Mask from CORINE 1992
- Shrub Land- Sparsely Vegetated Area
Artificial Surfaces Mask from CORINE 1992
Initial classification
After re- classification
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FINAL RESULTFINAL RESULT
Classification
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VALIDATIONVALIDATION
Reference: CORINE
EUROPEglc2000
MODIS
PELCOM
FRANCEglc2000
Confusion Matrix
Com
pari
son
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CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE
Validation
Accuracy =Correctly Detected Pixels
Total Detected Pixels from reference
FRANCE* : Grasslands & Forest+Pastures
66.8112.3056.5616.07Barren Land
43.4337.115.5642.7039.75Grasslands
39.03-27.7135.42Permanent Crop
80.7991.1880.7480.43Arable Land + Perm. Crop
80.2680.4880.5980.10Arable Land
17.4919.721.6914.74Shrubland
77.8247.1773.2370.77Forest
FRANCE
MODISEUROP
EPELCO
MFRANCE
*% (Accuracy 100)
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CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE
Validation
Reliability =(User’s accuracy)
Correctly Detected Pixels
Total Detected Pixels in the classified data
FRANCE* : Grasslands & Forest+Pastures
72.3192.5180.8988.40Barren Land
50.4357.7240.2352.1748.68Grasslands
52.67-55.7458.94Permanent Crop
60.4241.0051.7447.26Arable Land + Perm. Crop
63.1448.9153.1548.60Arable Land
35.417.0820.6437.01Shrubland
67.1349.9560.7352.26Forest
FRANCE
MODISEUROP
EPELCO
MFRANCE
*% (Reliability 100)
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CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE
Validation
Comparison Index (CI) = (Reliability Accuracy)0.5
FRANCE* : Grasslands & Forest+Pastures
0.700.340.680.38Barren Land
0.460.460.150.470.44Grasslands
0.450.090.390.46Permanent Crop
0.690.610.640.61Arable Land + Perm. Crop
0.710.630.650.62Arable Land
0.250.120.060.23Shrubland
0.720.490.670.61Forest
FRANCE
MODISEUROP
EPELCO
MFRANCE
*0 -1
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Reliability
CI
CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE
Validation
11.5413.7915.4712.45Mixed Forest
46.0557.5942.1130.60Coniferous Forest
49.3417.6733.3834.50Broad-leved Forest
FRANCE
MODISEUROP
EPELCO
M
12.4034.6415.0621.86Mixed Forest
63.3925.6851.4541.52Coniferous Forest
51.3313.5242.4342.08Broad-leved Forest
Accuracy
0.120.220.150.17Mixed Forest
0.540.390.470.36Coniferous Forest
0.500.160.380.38Broad-leved Forest
Forest…in
detail
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Overall Accuracy
Total
%
0 10 20 30 40 50 60 70 80 90 100
%
0 10 20 30 40 50 60 70 80 90 100
FRANCEMODISEUROPEPELCOM
CI x 100
Accuracy
Reliability50.94
57.51
59.83
61.7664.96
70.00
0.560.61
0.650.45
50.44
45.00
CONFUSION MATRIX WITH CORINECONFUSION MATRIX WITH CORINE
Validation
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Improvement of the classification due to the use of reflectances (B2,B3,SWIR) instead of NDVI
CONCLUSIONSCONCLUSIONS
Production of consistent normalized 10-day composites
Determination of new urban increase
Improvement of regional classification compared to the whole Europe (glc2000), PELCOM and MODIS products