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Titolo:
AUTOMATED CLOUD CLASSIFICATION OF GLOBAL AVHRR DATA USING A FUZZY-LOGIC APPROACH
Autore:
BAUM BA; TOVINKERE V; TITLOW J; WELCH RM;
Indirizzi:
NASA,LANGLEY RES CTR,DIV ATMOSPHER SCI,MS 420 HAMPTON VA 23681 SCI APPLICAT INT CORP HAMPTON VA 00000 S DAKOTA SCH MINES & TECHNOL,INST ATMOSPHER SCI RAPID CITY SD 00000
Titolo Testata:
Journal of applied meteorology
fascicolo: 11, volume: 36, anno: 1997,
pagine: 1519 - 1540
SICI:
0894-8763(1997)36:11<1519:ACCOGA>2.0.ZU;2-Q
Fonte:
ISI
Lingua:
ENG
Soggetto:
POLAR-REGIONS; SURFACE CLASSIFICATION; PATTERN-RECOGNITION; SPATIAL-RESOLUTION; TEXTURAL FEATURES; SATELLITE IMAGERY; NEURAL-NETWORK; EXPERT-SYSTEM; IDENTIFICATION; MISSION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
38
Recensione:
Indirizzi per estratti:
Citazione:
B.A. Baum et al., "AUTOMATED CLOUD CLASSIFICATION OF GLOBAL AVHRR DATA USING A FUZZY-LOGIC APPROACH", Journal of applied meteorology, 36(11), 1997, pp. 1519-1540

Abstract

A fuzzy logic classification (FLC) methodology is proposed to achievethe two goals of this paper: 1) to discriminate between clear sky andclouds in a 32 x 32 pixel array, or sample, of 1.1-km Advanced Very High Resolution Radiometer (AVHRR) data, and 2) if clouds are present, to discriminate between single-layered and multilayered clouds within the sample. To achieve these goals, eight FLC modules are derived thatare based broadly on airmass type and surface type (land or water): equatorial over land, marine tropical over land, marine tropical/equatorial over water, continental tropical over land, marine polar over land, marine polar over water, continental polar over land, and continental polar/arctic over water. Derivation of airmass type is performed using gridded analyses provided by the National Centers for Environmental Prediction. The training and testing data used by the FLC are collected from more than 150 daytime AVHRR local area coverage scenes recorded between 1991 and 1994 over all seasons and over all continents and oceans. A total of 190 textural and spectral features are computed from the AVHRR data. A forward feature selection method is implemented toreduce the number of features used to discriminate between classes ineach FLC module. The number of features selected ranges from 13 (marine tropical over land) to 24 (marine tropical/equatorial over water). An estimate of the classifier accuracy is determined using the hold-one-out method in which the classifier is trained with all but one of the data samples; the classifier is applied subsequently to the remaining sample. The overall accuracies of the eight classification modules are calculated by dividing the number of correctly classified samples by the total number of manually labeled samples of clear-sky and single-layer clouds. Individual module classification accuracies are as follows: equatorial over land (86.2%), marine tropical over land (85.6%), marine tropical/equatorial over water (88.6%), continental tropical over land (87.4%), marine polar over land (86.8%), marine polar over water (84.8%), continental polar over land (91.1%), and continental polar/arctic over water (89.8%). Single-level cloud samples misclassified as multilayered clouds range between 0.5% (continental polar over land)and 3.4% (marine polar over land) for the eight airmass modules. Classification accuracies for a set of labeled multilayered cloud samples range between 64% and 81% for six of the eight airmass modules (excluded are the continental polar over land and continental polar/arctic over water modules, for which multilayered cloud samples are difficult to find). The results indicate that the FLC has an encouraging ability to distinguish between single-level and multilayered clouds.

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Documento generato il 09/04/20 alle ore 20:13:42