u. amato +, l. cutillo *, v. cuomo o, c. serio x + istituto per le applicazioni del calcolo m....

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