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CLOUD DETECTION BY DISCRIMINANT ANALYSIS GERB and AVHRR case studies

This paper presents case studies using GERB and AVHRR data to detect clouds, using physical and statistical methods, including discriminant analysis. The goal is to develop tools for cloud clearing and cloud detection, with the aim of improving accuracy and merging physical and statistical models.

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CLOUD DETECTION BY DISCRIMINANT ANALYSIS GERB and AVHRR case studies

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  1. CLOUD DETECTION BY DISCRIMINANT ANALYSISGERB 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

  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. CLOUD DETECTIONPhysical 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

  4. 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

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

  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 mm) LW radiance ( > 4 mm)

  7. Test Success percentage (Linear Discriminant Analysis)

  8. Test Success percentage (Linear Discriminant Analysis)

  9. 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

  10. Test Success percentage (NonParametric Discriminant Analysis)

  11. Test Success percentage (Linear Discriminant Analysis)

  12. 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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