A simple and efficient approach for coarse segmentation of Moroccan coastal upwelling

In this work, we aim to develop a simple and fast algorithm using conventional methods in images segmentation for the automatic detection and extraction of upwelling areas, in the coastal region of Morocco, from the sea surface temperature (SST) satellite images. Our approach is based on the evaluation and comparison between two unsupervised classification methods, Otsu and Fuzzy C-means, and explores the applicability of these methods to our classification problem. The latter consists in coarse detection of the main thermal front that separates coastal cold upwelling waters from the remaining ocean waters. The algorithm has been applied and validated by an oceanographer over a database of 66 SST images corresponding to southern Moroccan coastal upwelling of the years 2004, 2005, 2007 and 2009. The results indicate that the proposed algorithm revealed is promising and reliable on different upwelling scenarios and for a wide variety of oceanographic conditions.

[1]  Boris Mirkin,et al.  Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science) , 2005 .

[2]  Ferran Marqués,et al.  Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[4]  Susana Nascimento,et al.  Automated computational delimitation of SST upwelling areas using fuzzy clustering , 2012, Comput. Geosci..

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Michalis Vazirgiannis,et al.  Clustering validity checking methods: part II , 2002, SGMD.

[7]  M. Olivar,et al.  Fronts and eddies as key structures in the habitat of marine fish larvae : opportunity , adaptive response and competitive advantage , 2006 .

[8]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[9]  Sarah H. Peckinpaugh,et al.  Edge detection applied to satellite imagery of the oceans , 1989 .

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  Peter Cornillon,et al.  Edge Detection Algorithm for SST Images , 1992 .

[12]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[13]  HalkidiMaria,et al.  Cluster validity methods , 2002 .

[14]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[15]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[16]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[17]  Carl G. Looney,et al.  A Fuzzy Clustering and Fuzzy Merging Algorithm , 2000 .

[18]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[19]  Félix de Moya Anegón,et al.  Comparison of neural models for document clustering , 2003, Int. J. Approx. Reason..

[20]  A. Bakun,et al.  Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage , 2006 .

[21]  Michalis Vazirgiannis,et al.  Cluster validity methods: part I , 2002, SGMD.

[22]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[23]  Antoine Mangin,et al.  Produits opérationnels d'océanographie spatiale pour le suivi et l'analyse du phénomène d'upwelling marocain , 2005 .

[24]  Emmanuel Chassot,et al.  Satellite remote sensing for an ecosystem approach to fisheries management , 2011 .

[25]  Swapnil Chaudhari,et al.  Upwelling Detection in AVHRR Sea Surface Temperature (SST) Images using Neural-Network Framework , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Boca Raton,et al.  Clustering for Data Mining , 2005 .

[27]  Jacek M. Leski,et al.  Towards a robust fuzzy clustering , 2003, Fuzzy Sets Syst..

[28]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .