Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Applied Soft Computing

Volumn 30, Issue , 2015, Pages 1-13

  Iounousse, Jawad ^a   Er Raki, Salah ^a   El Motassadeq, Ahmed ^a   Chehouani, Hassan ^a  

^a CADI AYYAD UNIVERSITY   (Morocco)

Author keywords

Cluster validity index; Land use; LANDSAT and SPOT images; Probabilistic Neural Network; Unsupervised classification; Ward's method

Indexed keywords

FUZZY CLUSTERING; IMAGE CLASSIFICATION; LAND USE;

CLUSTER VALIDITY INDICES; PROBABILISTIC NEURAL NETWORKS; SPOT IMAGE; UNSUPERVISED CLASSIFICATION; WARD'S METHOD;

NEURAL NETWORKS;

EID: 84922519910     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.01.037     Document Type: Article

References (81)

1. L. Zheng, and X. He Classification techniques in pattern recognition Proceedings of the 13th International Conference in Central Europe on Computer Graphics, Visualization and computer vision (WSCG 2005) 2005 77 78
2. V. Mohan, and A. Kannan Color image classification and retrieval using image mining techniques Int. J. Eng. Sci. Technol. 2 5 2010 1014 1020
3. T.N. Phyu Survey of classification techniques in data mining Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2009) 2009 978 988
4. J.S. Wang, W.C. Chiang, Y.L. Hsu, and Y.T.C. Yang ECG arrhythmia classification using a probabilistic neural network with a feature reduction method Neurocomputing 116 2013 38 45
5. J.I. Arribas, G.V. Sánchez-Ferrero, G. Ruiz-Ruiz, and J. Gómez-Gil Leaf classification in sunflower crops by computer vision and neural networks Comput. Electron. Agric. 78 1 2011 9 18
6. R. Raghuraj, and S. Lakshminarayanan Variable predictive model based classification algorithm for effective separation of protein structural classes Comput. Biol. Chem. 32 4 2008 302 306
7. F. Kaefer, C.M. Heilman, and S.D. Ramenofsky A neural network application to consumer classification to improve the timing of direct marketing activities Comput. Oper. Res. 32 10 2005 2595 2615
8. N. Huang, D. Xu, X. Liu, and L. Lin Power quality disturbances classification based on S-transform and probabilistic neural network Neurocomputing 98 2012 12 23
9. Y. Julien, J.A. Sobrino, and J.-C. Jiménez-Munoz Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method Int. J. Appl. Earth Obs. Geoinf. 13 2011 711 720
10. R. Geerken, B. Zaitchik, and J.P. Evans Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity Int. J. Remote Sens. 26 24 2005 5535 5554
11. A. Halder, A. Ghosh, and S. Ghosh Supervised and unsupervised land use map generation from remotely sensed images using ant based systems Appl. Soft Comput. 11 2011 5770 5781
12. J. Sun, J. Yang, C. Zhang, W. Yun, and J. Qu Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method Math. Comput. Modell. 58 3-4 2013 573 581
13. Q. Lü, and M. Tang Detection of hidden bruise on kiwi fruit using hyperspectral imaging and parallelepiped classification Procedia Environ. Sci. 12 B 2012 1172 1179
14. C. Chen Fuzzy training data for fuzzy supervised classification of remotely sensed images Proceedings of 20th Asian Conference on Remote Sensing (ACRS 1999) 1999 460 465
15. A. Ghosh, S. Meher, and B.U. Shankar A novel fuzzy classifier based on product aggregation operator Pattern Recognit. 41 6 2008 961 971
16. F. Maselli, A. Rodolfi, and C. Copnese Fuzzy classification of spatially degraded thematic Mapper data for the estimation of sub-pixel components Int. J. Remote Sens. 17 3 1996 537 551
17. F. Melgani, B.A. Hashemy, and S. Taha An explicit fuzzy supervised classification method for multispectral remote sensing images IEEE Trans. Geosci. Remote Sens. 38 1 2000 287 295
18. Y. Liu, B. Zhang, L.-m. Wang, and N. Wang A self-trained semisupervised SVM approach to the remote sensing land cover classification Comput. Geosci. 59 2013 98 107
19. D.M. Miller, E.J. Kaminsky, and S. Rana Neural network classification of remote-sensing data Comput. Geosci. 21 1995 377 386
20. J. Zeng, H.-f. Guo, and Y.-m. HU Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission J. Environ. Sci. 19 2007 427 431
21. M. Brown, H. Lewis, and S. Gunn Linear spectral mixture models and support vector machines for remote sensing IEEE Trans. Geosci. Remote Sens. 38 5 2000 2346 2360
22. C. Huang, L. Davis, and J. Townshend An assessment of support vector machines for land cover classification Int. J. Remote Sens. 23 2002 725 749
23. D. Stathakis, and A. Vasilakos Comparison of computational intelligence based classification techniques for remotely sensed optical image classification IEEE Trans. Geosci. Remote Sens. 44 8 2008 2305 2318
24. R. Laprade Split-and-merge segmentation of aerial photographs Comput. Vis. Graphics Image Process. 48 1 1988 77 86
25. B.J. Irvin, S.J. Ventura, and B.K. Slater Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley Wisconsin Geoderma 77 2-4 1997 137 154
26. S. Pal, A. Ghosh, and B. Uma Shankar Segmentation of remotely sensed images with fuzzy thresholding and quantitative evaluation Int. J. Remote Sens. 21 11 2000 2269 2300
27. R. Cannon, R. Dave, J. Bezdek, and M. Trivedi Segmentation of a thematic Mapper image using fuzzy c-means clustering algorithm IEEE Trans. Geosci. Remote Sens. 24 1 1986 400 408
28. M.N. Kurnaz, Z. Dokur, and T. Olmez Segmentation of remote-sensing images by incremental neural network Pattern Recogn. Lett. 26 8 2005 1104 1316
29. Z. Zhou, S. Wei, X. Zhang, and X. Zhao Remote sensing image segmentation based on self-organizing map at multiple scale Proceedings of SPIE Geoinformatics: Remotely Sensed Data and Information USA 2007 122 126
30. Y. Wong, and E. Posner A new clustering algorithm applicable to polarimetric and SAR images IEEE Trans. Geosci. Remote Sens. 31 3 1993 634 644
31. G.P. Zhang Neural networks for classification: a survey IEEE Trans. Syst. Man Cybernet. C: Appl. Rev. 30 4 November 2000
32. G. Cybenko Approximation by superpositions of a sigmoidal function Math. Control. Signals Syst. 2 1989 303 314
33. K. Hornik Approximation capabilities of multilayer feedforward networks Neural Networks 4 1991 251 257
34. K. Hornik, M. Stinchcombe, and H. White Multilayer feedforward networks are universal approximators Neural Networks 2 1989 359 366
35. M.D. Richard, and R. Lippmann Neural network classifiers estimate Bayesian a posteriori probabilities Neural Comput. 3 1991 461 483
36. D.F. Specht Probabilistic neural networks for classification, mapping, or associative memory IEEE International Conference on Neural Networks 1 July 1988 525 532
37. D.F. Specht Probabilistic neural networks Neural Networks 3 1 1990 109 118
38. T.D. Gancheva, D.K. Tasoulisb, M.N. Vrahatisb, and N.D. Fakotakis Generalized locally recurrent probabilistic neural networks with application to text-independent speaker verification Neurocomputing 70 2007 1424 1438
39. X. Fu, Y. Ying, Y. Zhou, and H. Xu Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats Anal. Chim. Acta 598 2007 27 33
40. A.L.I. Oliveira, F.R.G. Costa, and C.O.S. Filho Novelty detection with constructive probabilistic neural networks Neurocomputing 71 2008 1046 1053
41. J. Grim, and J. Hora Iterative principles of recognition in probabilistic neural networks Neural Networks 21 2008 838 846
42. H. Adeli, and A. Panakkat A probabilistic neural network for earthquake magnitude prediction Neural Networks 22 2009 1018 1024
43. F. Ozturk, and F. Ozen A new license plate recognition system based on probabilistic neural networks Procedia Technol. 1 2012 124 128
44. O. Er, A.C. Tanrikulu, A. Abakay, and F. Temurtas An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease Comput. Electr. Eng. 38 2012 75 81
45. J. Jia, C. Liang, J. Cao, and Z. Li Application of probabilistic neural network in bacterial identification by biochemical profiles J. Microbiol. Methods 94 2013 86 87
46. S. Timung, and T.K. Mandal Prediction of flow pattern of gas-liquid flow through circular microchannel using probabilistic neural network Appl. Soft Comput. 13 2013 1674 1685
47. G.E. Tsekouras, and J. Tsimikas On training RBF neural networks using input-output fuzzy clustering and particle swarm optimization Fuzzy Sets Syst. 221 2013 65 89
48. J. González, I. Rojas, H. Pomares, J. Ortega, and A. Prieto A new clustering technique for function approximation IEEE Trans. Neural Networks 13 1 2002 132 142
49. H.-S. Park, W. Pedrycz, and S.-K. Oh Granular neural networks and their development through context-based clustering and adjustable dimensionality of receptive fields IEEE Trans. Neural Networks 20 10 2009 1604 1616
50. W. Pedrycz Conditional fuzzy clustering in the design of radial basis function neural networks IEEE Trans. Neural Networks 9 4 1998 601 612
51. W. Pedrycz, H.S. Park, and S.K. Oh A granular-oriented development of functional radial basis function neural networks Neurocomputing 72 2008 420 435
52. H.-S. Park, Y.-D. Chung, S.-K. Oh, W. Pedrycz, and H.-K. Kim Design pf information granule-oriented RBF neural networks and its application to power supply for high-field magnet Eng. Appl. Artif. Intell. 24 2011 543 554
53. S.B. Roh, T.C. Ahn, and W. Pedrycz The design methodology of radial basis function neural networks based on fuzzy K-nearest neighbors approach Fuzzy Sets Syst. 161 13 2010 1803 1822
54. A. Staiano, R. Tagliaferri, and W. Pedrycz Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering Neurocomputing 69 2006 1570 1581
55. Z. Uykan, C. Guzelis, M.E. Celebi, and H.N. Koivo Analysis of input-output clustering for determining centers of RBFNN IEEE Trans. Neural Networks 11 4 2000 851 858
56. J.H. Ward Hierarchical grouping to optimize an objective function J. Am. Stat. Assoc. 58 1963 236 244
57. A.M. Dillner, J.J. Schauer, W.F. Christensen, and G.R. Cass A quantitative method for clustering size distributions of elements Atmos. Environ. 39 2005 1525 1537
58. H.C. Lu, C.L. Chang, and J.C. Hsieh Classification of PM10 distributions in Taiwan Atmos. Environ. 40 2006 1452 1463
59. T. Varin, R. Bureau, C. Mueller, and P. Willett Clustering files of chemical structures using the Székely-Rizzo generalization of Ward's method J. Mol. Graphics Modell. 28 2009 187 195
60. N. Picard, F. Mortier, V. Rossi, and S. Gourlet-Fleury Clustering species using a model of population dynamics and aggregation theory Ecol. Modell. 221 2010 152 160
61. A. Carteron, M. Jeanmougin, F. Leprieur, and S. Spatharis Assessing the efficiency of clustering algorithms and goodness-of-fit measures using phytoplankton field data Ecol. Inform. 9 2012 64 68
62. C.S. Malley, C.F. Braban, and M.R. Heal The application of hierarchical cluster analysis and non-negative matrix factorization to European atmospheric monitoring site classification Atmos. Res. 138 2014 30 40
63. Y. Xiao, C. Mignolet, J.-F. Mari, and M. Benoît Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: a case from France Comput. Electron. Agric. 102 2014 51 63
64. S. Hands, and B. Everitt A Monte Carlo study of the recovery of cluster structure in binary data by hierarchical clustering techniques Multivar. Behav. Res. 22 1987 235 243
65. A. Ghosh, N.S. Mishra, and S. Ghosh Fuzzy clustering algorithms for unsupervised change detection in remote sensing images Inform. Sci. 181 2011 699 715
66. W. Wang, and Y. Zhang On fuzzy cluster validity indices Fuzzy Sets Syst. 158 2007 2095 2117
67. K.-L. Wu, and M.-S. Yang A cluster validity index for fuzzy clustering Pattern Recogn. Lett. 26 2005 1275 1291
68. A. Chehbouni, R. Escadafal, G. Boulet, B. Duchemin, V. Simonneaux, G. Dedieu, B. Mougenot, S. Khabba, H. Kharrou, O. Merlin, A. Chaponnière, J. Ezzahar, S. Er-Raki, J. Hoedjes, R. Hadria, H. Abourida, A. Cheggour, F. Raibi, L. Hanich, N. Guemouria, Ah. Chehbouni, A. Lahrouni, A. Olioso, F. Jacob, and J. Sobrino The use of remotely sensed data for integrated hydrological modeling in arid and semi-arid regions: the SUDMED program Int. J. Remote Sens. 29 2008 5161 5181
69. R. Hadria, B. Duchemin, A. Lahroun, S. Khabba, S. Er-Raki, G. Dedieu, and A. Chehbouni Monitoring of irrigated wheat in a semi-arid climate using crop modelling and remote sensing data: impact of satellite revisit time frequency Int. J. Remote Sens. 27 2006 1093 1117
70. B. Duchemin, R. Hadria, S. Erraki, G. Boulet, P. Maisongrande, A. Chehbouni, R. Escadafal, J. Ezzahar, J.C.B. Hoedjes, M.H. Kharrou, S. Khabba, B. Mougenot, A. Olioso, J.C. Rodriguez, and V. Simonneaux Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely sensed vegetation indices Agric. Water Manage. 79 2006 1 27
71. S. Er-Raki, A. Chehbouni, N. Guemouria, B. Duchemin, J. Ezzahar, and R. Hadria Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region Agric. Water Manage. 87 2007 41 54
72. V. Simonneaux, B. Duchemin, D. Helson, S. Er-Raki, A. Olioso, and A.G. Chehbouni The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco Int. J. Remote Sens. 29 2008 95 116
73. G.M. Foody Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches J. Geoghraph. Syst. 3 2001 217 232
74. R.N. Dave Validating fuzzy partition obtained through c-shells clustering Pattern Recogn. Lett. 17 1996 613 623
75. R.A. Fisher The use of multiple measurements in taxonomic problems Ann. Eugenics 7 2 1936 179 188
76. E. Anderson The species problem in Iris Ann. MO Bot. Gard. 23 3 1936 457 509
77. VALERI (Validation of Land European Remote sensing Instruments) Program, http://w3.avignon.inra.fr/valeri/.
78. D. Lloyd A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery Int. J. Remote Sens. 11 1990 2269 2279
79. R.S. De Fries, M. Hansen, J.R.G. Townshend, and R. Sohlberg Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers Int. J. Remote Sens. 19 1998 3141 3168
80. S.S. Ray, and V.K. Dadhwal Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS Agric. Water Manage. 49 2001 239 249
81. R. Congalton, and K. Green Assessing the Accuracy of Remotely Sensed Data 1999 Lewis Publications Boca Raton, FL