CLOUD DETECTION BY
DISCRIMINANT ANALYSIS
GERB and AVHRR case studies
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
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
CLOUD DETECTION
Physical methods
 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 DETECTION
Statistical 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
Training set
Multispectral
images
DISCRIMINANT
ANALYSIS
Nonparametric
density
estimation
Data
transformation
Cloud
detection
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 mm)
LW radiance ( > 4 mm)
Test
Latitude
Longitude
Time
Day
Train
[-45o,+60o]
[-20o,+60o]
16:00
Jun 21st 2001
Test
[-45o,+60o]
[-20o,+60o]
16:00
Jun 21st 2001
Success percentage (Linear Discriminant Analysis)
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
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
Success percentage (Linear Discriminant Analysis)
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
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 mm, 0.91 mm, 3.74 mm, 10.8 mm, 11.5 mm
Test
Latitude
Longitude
Day
Train
[-45o,+60o]
[-20o,+60o]
Dec 21st 2001
Test
[+30o,+55o]
[0o,+25o]
Jun 21st 2001
Success percentage (NonParametric Discriminant Analysis)
Clear
Cloudy
Total
Land - 0.63 mm
93.0
100
94.6
Land - 0.91 mm
67.9
99.4
75.3
Land – 3.74 mm
29.0
72.5
39.2
Land – 10.8 mm
67.8
100
75.4
Land – 11.5 mm
85.8
75.7
83.4
Test
Latitude
Longitude
Day
Train
[-45o,+60o]
[-20o,+60o]
Jun 21st 2001
Test
[+30o,+55o]
[0o,+25o]
Dec 21st 2001
Success percentage (Linear Discriminant Analysis)
Clear
Cloudy
Total
Land - 0.63 mm
97.0
97.6
97.2
Land - 0.91 mm
66.8
99.9
74.6
Land – 3.74 mm
35.2
0.3
27.0
Land – 10.8 mm
72.9
99.3
79.1
Land – 11.5 mm
72.5
99.8
78.9
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