UKCI’05 5-7 September 1 Applicability of Fuzzy Clustering Applicability of Fuzzy Clustering for the for the Identification of Upwelling Areas Identification of Upwelling Areas on Sea Surface Temperature Images on Sea Surface Temperature Images Susana Nascimento, Fátima M. Sousa, Hugo Casimiro Dmitri Boutov 2 Instituto de Oceanografia Faculdade de Ciências Universidade de Lisboa, PORTUGAL 1 Centro de Inteligência Artificial Dep. Informática Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa PORTUGAL
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UKCI05 5-7 September 1 Applicability of Fuzzy Clustering for the Identification of Upwelling Areas on Sea Surface Temperature Images Susana Nascimento,
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UKCI’05 5-7 September 1
Applicability of Fuzzy Clustering for Applicability of Fuzzy Clustering for thethe
Identification of Upwelling Areas on Identification of Upwelling Areas on
Sea Surface Temperature ImagesSea Surface Temperature Images Susana Nascimento, Fátima M. Sousa,
Hugo Casimiro Dmitri Boutov
2Instituto de Oceanografia
Faculdade de Ciências
Universidade de Lisboa,
PORTUGAL
1
Centro de Inteligência Artificial
Dep. InformáticaFaculdade de Ciências e Tecnologia
Universidade Nova de LisboaPORTUGAL
UKCI’05 5-7 September 2
Overview
Introduction to the problem of Upwelling Recognition
Sea Surface Temperature (SST) Image Segmentation by Fuzzy Partitional Clustering
Methodology
Experimental Study
Ongoing Work
UKCI’05 5-7 September 3
Upwelling Event
What is Upwelling?
It is a mass of deep, cold, and nutrient-rich seawater that rises close to the coast.
Upwelling occurs when winds parallel to the coast induce a net mass transport of surface seawater in a 90º direction, away from the coast, due to the Coriolis force. Deep waters rise in order to compensate the mass deficiency that develops along the coastal area.
Why is Upwelling so important? Brings nutrient-rich deep waters close to the ocean
surface, creating regions of high biological productivity. Strong impact on fisheries, and global oceanic climate
SST image of an upwelling event obtained on 04AUG1998 (n14_98216_0422_sst); (b) upwelling boundary manually contoured; (c) upwelling areas automatically retrieved.
Ground truth image
UKCI’05 5-7 September 5
Why an Automatic System for Upwelling Recognition?
Satellite Station of Instituto de Oceanografia (IO) of FC-UL Reception AVHRR thermal infrared Images since 1991
100 images per Upwelling Epoc (June-September) An expert chooses, by visual inspection, the best image of a day
reception and treatment of 3-4 images a day.
Until now, the areas covered by upwelling waters including cold filaments, have been contoured by hand.
The method is very subjective and depending on the skill and practice of the expert.
UKCI’05 5-7 September 6
Data
AVHRR thermal infrared images are received and processed by IO Station with SeaSpace software package TeraScan producing SST images.
Sea Surface Temperature (SST) images
720 400 matrix with each entry a temperature value in degrees Celsius with 1Km2 spatial resolution.
X
Y
UKCI’05 5-7 September 7
Distinct Groups of Images
(G1) well-defined upwelling events
(G2) images where upwelling is evident but there are areas with no temperature information (covered with clouds or noise);
SST images divided into 5 groups according to different “upwelling situations”.
(G5) Images lacking the upwelling event
(G4) 3-day sequence of an upwelling event
(G3) Upwelling event not well-defined;
UKCI’05 5-7 September 8
Nature of the problem is Fuzzy
Unsupervised segmentation does not require training data.
Expert´s can take advantage of visualization skills and interpretability of fuzzy membership values.
Why SST Image Segmentation by Fuzzy Clustering?
Upwelling frontier
UKCI’05 5-7 September 9
Methodology
Feature Extraction
Image compression/data quantization
Fuzzy Clustering Segmentation
Accuracy Assessment
Fuzzy Clustering
VisualizationFuzzy
Partition
Pixel aggregation
Region quantization
UKCI’05 5-7 September 10
Fuzzy Clustering
k-means vs Fuzzy c-means FCM AO Algorithms
Fuzzy c-Means (FCM)
• Validity Guided (re)Clustering
• Adaptive variants
• ...
Parameters 1. sharpness exponent m, 2. number of clusters ‘c’
FCM FeaturesData representation: objects are vectors of measured values.
Clusters shape: different geometric prototypes; norms or scalar products.
Clusters size: use of adaptive distance or adaptive algorithms.
Clusters validity: optimal number of classes through validity functionals,
clusters merging/splitting or by using a hierarchical approach.
Final fuzzy partition: can be defuzzied; fuzzy partition should not be discarded
Method: fuzzy objective function minimization; two step iterativeprocedure that continually decreases the value of the objective function
FCM FeaturesData representation: objects are vectors of measured values.
Clusters shape: different geometric prototypes; norms or scalar products.
Clusters size: use of adaptive distance or adaptive algorithms.
Clusters validity: optimal number of classes through validity functionals,
clusters merging/splitting or by using a hierarchical approach.
Final fuzzy partition: can be defuzzied; fuzzy partition should not be discarded
Method: fuzzy objective function minimization; two step iterativeprocedure that continually decreases the value of the objective function