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U. Amato +, L. Cutillo *, V. Cuomo o, C. Serio x + Istituto per le Applicazioni del Calcolo M. Picone CNR, Napoli, Italy * Dipartimento di Matematica e.

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Presentation on theme: "U. Amato +, L. Cutillo *, V. Cuomo o, C. Serio x + Istituto per le Applicazioni del Calcolo M. Picone CNR, Napoli, Italy * Dipartimento di Matematica e."— Presentation transcript:

1 U. Amato +, L. Cutillo *, V. Cuomo o, C. Serio x + Istituto per le Applicazioni del Calcolo M. Picone CNR, Napoli, Italy * Dipartimento di Matematica e Applicazioni, Università di Napoli Federico II, Italy o Istituto di Metodologie di Analisi Ambientale CNR, Potenza, Italy x Dipartimento 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 5 th 2003

2 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

3 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 Physical methods

4 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

5 Multispectral images Cloud mask DISCRIMINANT ANALYSIS Multispectral images Cloud detection Nonparametric density estimation Data transformation Training set

6 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)

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9 LatitudeLongitudeTimeDay Train[-45 o,+60 o ][-20 o,+60 o ]16:00Jun 21 st 2001 Test[-45 o,+60 o ][-20 o,+60 o ]16:00Jun 21 st 2001 ClearCloudyTotal Sea - SW82.295.892.7 Sea - LW86.656.563.4 Land - SW82.579.682.0 Land - LW83.952.478.8 Success percentage (Linear Discriminant Analysis) Test

10 LatitudeLongitudeTimeDay Train[-45 o,+60 o ][-20 o,+60 o ]16:00Jun 21 st 2001 Test[-30 o,+55 o ][0 o,+25 o ]12:00Feb 8 th 2001 ClearCloudyTotal Sea - SW76.497.188.6 Sea - LW85.737.857.6 Land - SW98.344.485.7 Land - LW84.966.980.7 Success percentage (Linear Discriminant Analysis) Test

11 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

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13 LatitudeLongitudeDay Train[-45 o,+60 o ][-20 o,+60 o ]Dec 21 st 2001 Test[+30 o,+55 o ][0 o,+25 o ]Jun 21 st 2001 ClearCloudyTotal Land - 0.63 m 93.010094.6 Land - 0.91 m 67.999.475.3 Land – 3.74 m 29.072.539.2 Land – 10.8 m 67.810075.4 Land – 11.5 m 85.875.783.4 Success percentage (NonParametric Discriminant Analysis) Test

14 LatitudeLongitudeDay Train[-45 o,+60 o ][-20 o,+60 o ]Jun 21 st 2001 Test[+30 o,+55 o ][0 o,+25 o ]Dec 21 st 2001 ClearCloudyTotal Land - 0.63 m 97.097.697.2 Land - 0.91 m 66.899.974.6 Land – 3.74 m 35.20.327.0 Land – 10.8 m 72.999.379.1 Land – 11.5 m 72.599.878.9 Success percentage (Linear Discriminant Analysis) Test

15 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


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