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U. Amato+, L. Cutillo*, V. Cuomoo, C. Seriox
+Istituto per le Applicazioni del Calcolo ‘M. Picone’ CNR, Napoli, Italy
*Dipartimento di Matematica e Applicazioni, Università di Napoli ‘Federico II’, Italy
oIstituto di Metodologie di Analisi Ambientale CNR, Potenza, Italy
xDipartimento di Ingegneria e Fisica Ambientale, Università della Basilicata, Potenza, Italy
CLOUD DETECTION BY DISCRIMINANT ANALYSIS
GERB and AVHRR case studies
GIST-17 Meeting, London, February 5th 2003
Plans to use GERB/SEVIRI data
•Case Study: Desertification processes in Southern Italy
•Methodology: Energy Balance at the Surface
•Tools to be developed: (Among Others) Cloud Clearing and Cloud detection
Physical methods mainly based on thresholds evaluated by Radiative Transfer models
Criteria for cloud detection often based on couples of reflectance/radiances at different wavelengths
Multispectral and hyperspectral sensors potentially increase accuracy of cloud detection, but pose new challenges to the algorithm development
CLOUD DETECTIONPhysical methods
CLOUD DETECTIONStatistical methods
Discriminant Analysis methods Nonparametric estimate of the radiance/reflectance density functions
Transform of the radiance/reflectance multispectral components into new components (e.g., Principal Component Analysis, PCA; Independent Component Analysis, ICA)
Classification by a classical Bayes rule
Multispectral images
Cloud mask
DISCRIMINANT ANALYSIS
Multispectral images
Cloud detection
Nonparametric density
estimation
Data transformation
Training setTraining set
Case study: GERB
GERB-like data, format ARCH
60-minutes snapshots
Full-disk
Spatial resolution: about 33% of the 3x3 SEVIRI grid (833x833 pixels, 3Km x 3Km at the sub-satellite point)
SW radiance ( < 4 m)
LW radiance ( > 4 m)
Latitude Longitude Time Day
Train [-45o,+60o] [-20o,+60o] 16:00 Jun 21st 2001
Test [-45o,+60o] [-20o,+60o] 16:00 Jun 21st 2001
Clear Cloudy Total
Sea - SW 82.2 95.8 92.7
Sea - LW 86.6 56.5 63.4
Land - SW 82.5 79.6 82.0
Land - LW 83.9 52.4 78.8
Success percentage (Linear Discriminant Analysis)
Test
Latitude Longitude Time Day
Train [-45o,+60o] [-20o,+60o] 16:00 Jun 21st 2001
Test [-30o,+55o] [0o,+25o] 12:00 Feb 8th 2001
Clear Cloudy Total
Sea - SW 76.4 97.1 88.6
Sea - LW 85.7 37.8 57.6
Land - SW 98.3 44.4 85.7
Land - LW 84.9 66.9 80.7
Success percentage (Linear Discriminant Analysis)
Test
Case study: AVHRR
AVHRR onboard of NOAA 14
Full-disk
Spatial resolution: 8 Km x 8 Km at the sub-satellite point
5 channels: 0.63 m, 0.91 m, 3.74 m, 10.8 m, 11.5 m
Latitude Longitude Day
Train [-45o,+60o] [-20o,+60o] Dec 21st 2001
Test [+30o,+55o] [0o,+25o] Jun 21st 2001
Clear Cloudy Total
Land - 0.63 m 93.0 100 94.6
Land - 0.91 m 67.9 99.4 75.3
Land – 3.74 m 29.0 72.5 39.2
Land – 10.8 m 67.8 100 75.4
Land – 11.5 m 85.8 75.7 83.4
Success percentage (NonParametric Discriminant Analysis)
Test
Latitude Longitude Day
Train [-45o,+60o] [-20o,+60o] Jun 21st 2001
Test [+30o,+55o] [0o,+25o] Dec 21st 2001
Clear Cloudy Total
Land - 0.63 m 97.0 97.6 97.2
Land - 0.91 m 66.8 99.9 74.6
Land – 3.74 m 35.2 0.3 27.0
Land – 10.8 m 72.9 99.3 79.1
Land – 11.5 m 72.5 99.8 78.9
Success percentage (Linear Discriminant Analysis)
Test
Perspectives
To make density functions of radiance/reflectance least depending on time and location
To choose a proper transform of multispectral data aimed at picking essential information and eliminating redundancies
To merge physical and statistical models into a mixed model able to share benefits of both