Suspended Matter Model on Alsat-1 Image by MLP Network and Mathematical Morphology: Prototypes by K-Means
In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.
[1] D. K Hall., J. R. Key, Casey K. A., G. A. Riggs , D. J. Cavalieri., " Sea
Ice Surface Temperature Product from MODIS," IEEE Trans.
Geoscience Remote Sensing, , 2004, vol. 42, no. 5, May pp. 1076-1087.
[2] L. Parkinson, "Aqua: An Earth-Observing Satellite Mission to Examine
Water and Other Climate Variables," IEEE Trans Geoscience Remote
Sensing, 2003, vol. 41, no. 2, Feb pp. 173-183.
[3] J. M Froidefond , D. Doxaran, " Télédétection Optique Appliquée ├á
l-étude de eaux c├┤tières," Télédétection, 2004, vol. 4, no. 2, pp. 157-
174.
[4] H. Merrad, "Caractérisation des Eaux C├┤tières ├á partir d-Images Multi
spectrales de MODIS et ALSAT-1 : Application au littoral Algérien, "
Thèse de Magister, 2005, Université des Sciences et de la Technologie
Houari Boumediene à Alger,.
[5] M. Bekhti, A. Oussedik, J.R. Cooksley. " Alsat-1: Conception details
and in orbit performance, " Actes des Journées Techniques ALSAT 1/
Utilisateurs, 2003, Juillet 14 et 15 , Alger.
[6] D.B. Patissier, G.H. Tilstone, V.M. Vincente & G.F. Moore,
"Comparaison of bio-physical marine products from SeaWiFs, MODIS
and a bio-optical model with in situ measurements from Northern
European waters," .2004, Journal of Opitcs, pp. 875-889.
[7] D. Doxaran, J. M. Froidefond, S Lavender & P. Castaing, "Spectral
signature of highly turbid waters Application with SPOT data to
quantify suspended particulate matter," Remote Sensing of
Environment, 2002,81, 149-161,
[8] M.Coster ET J. L.Cherman, "Précis d'analyse d'images," 1985,
Éditions du CNRS.
[9] ] J. Serra. "Image Analysis and Mathematical Morphology," 1982
Academic Press, London.S.
[10] Haykin, "Neural Networks: A Comprehensive Foundation," 1999
Second edition, Prentice Hall.
[11] F. Alilat, S. Loumi, H. Merrad & B. Sansal, "Nouvelle approche du
réseau ARTMAP Flou Application ├á la classification multispectrale des
images SPOT XS de la baie d-Alger," Revue Française de
Photogramétrie et de Télédétection SFPT, (2005-1),.no.177, pp 17-24.
[12] Z. Zhou, Chen & Z. Chen, "FANNC: A Fast Adaptive Neural Network
classifier," 2000, Knowledge and Information Systems, Vol. 2, N┬░1, pp.
115-129.
[13] P. Baldi, "Gradient Descent Learning Algorithm Overview: A General
Dynamical Systems Perspective," 1995, IEEE Transactions on Neural
Networks, Vol. 6.
[14] M. Hagan T. & M .B. Menhaj, "Training Feed forward Networks with
the Marquardt Algorithm,", 1994, IEEE Transactions on Neural
Networks, Vol.5 No 6.
[15] B.M. Wilamowskin, S. Iplikci, O. Kaynak & Efe, "An Algorithm for
fast Convergence in Training Neural Networks," 2001, IJCNN-01,
Washington C. c July 15-19 , pp. 1778-1782.
[1] D. K Hall., J. R. Key, Casey K. A., G. A. Riggs , D. J. Cavalieri., " Sea
Ice Surface Temperature Product from MODIS," IEEE Trans.
Geoscience Remote Sensing, , 2004, vol. 42, no. 5, May pp. 1076-1087.
[2] L. Parkinson, "Aqua: An Earth-Observing Satellite Mission to Examine
Water and Other Climate Variables," IEEE Trans Geoscience Remote
Sensing, 2003, vol. 41, no. 2, Feb pp. 173-183.
[3] J. M Froidefond , D. Doxaran, " Télédétection Optique Appliquée ├á
l-étude de eaux c├┤tières," Télédétection, 2004, vol. 4, no. 2, pp. 157-
174.
[4] H. Merrad, "Caractérisation des Eaux C├┤tières ├á partir d-Images Multi
spectrales de MODIS et ALSAT-1 : Application au littoral Algérien, "
Thèse de Magister, 2005, Université des Sciences et de la Technologie
Houari Boumediene à Alger,.
[5] M. Bekhti, A. Oussedik, J.R. Cooksley. " Alsat-1: Conception details
and in orbit performance, " Actes des Journées Techniques ALSAT 1/
Utilisateurs, 2003, Juillet 14 et 15 , Alger.
[6] D.B. Patissier, G.H. Tilstone, V.M. Vincente & G.F. Moore,
"Comparaison of bio-physical marine products from SeaWiFs, MODIS
and a bio-optical model with in situ measurements from Northern
European waters," .2004, Journal of Opitcs, pp. 875-889.
[7] D. Doxaran, J. M. Froidefond, S Lavender & P. Castaing, "Spectral
signature of highly turbid waters Application with SPOT data to
quantify suspended particulate matter," Remote Sensing of
Environment, 2002,81, 149-161,
[8] M.Coster ET J. L.Cherman, "Précis d'analyse d'images," 1985,
Éditions du CNRS.
[9] ] J. Serra. "Image Analysis and Mathematical Morphology," 1982
Academic Press, London.S.
[10] Haykin, "Neural Networks: A Comprehensive Foundation," 1999
Second edition, Prentice Hall.
[11] F. Alilat, S. Loumi, H. Merrad & B. Sansal, "Nouvelle approche du
réseau ARTMAP Flou Application ├á la classification multispectrale des
images SPOT XS de la baie d-Alger," Revue Française de
Photogramétrie et de Télédétection SFPT, (2005-1),.no.177, pp 17-24.
[12] Z. Zhou, Chen & Z. Chen, "FANNC: A Fast Adaptive Neural Network
classifier," 2000, Knowledge and Information Systems, Vol. 2, N┬░1, pp.
115-129.
[13] P. Baldi, "Gradient Descent Learning Algorithm Overview: A General
Dynamical Systems Perspective," 1995, IEEE Transactions on Neural
Networks, Vol. 6.
[14] M. Hagan T. & M .B. Menhaj, "Training Feed forward Networks with
the Marquardt Algorithm,", 1994, IEEE Transactions on Neural
Networks, Vol.5 No 6.
[15] B.M. Wilamowskin, S. Iplikci, O. Kaynak & Efe, "An Algorithm for
fast Convergence in Training Neural Networks," 2001, IJCNN-01,
Washington C. c July 15-19 , pp. 1778-1782.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:61625", author = "S. Loumi and H. Merrad and F. Alilat and B. Sansal", title = "Suspended Matter Model on Alsat-1 Image by MLP Network and Mathematical Morphology: Prototypes by K-Means", abstract = "In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.", keywords = "Classification K-means, mathematical morphology,
neural network MLP, remote sensing, suspended particulate matter", volume = "1", number = "6", pages = "276-8", }