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

Using of Probabilistic Neural Network (PNN) to multichannel image classification.Automatic process to transform the supervised PNN classification to unsupervised.Combination of PNN, hierarchical clustering and cluster validity Index.Land use classification gives a precision with about 3.44% of error.Can be used to large surfaces where the information on soil and crops is limited. The aim of this work is to develop an unsupervised approach based on Probabilistic Neural Network (PNN) for land use classification. A time series of high spatial resolution acquired by LANDSAT and SPOT images has been used to firstly generate the profiles of Normalized Difference Vegetation Index (NDVI) and then used for the classification procedure.The proposed method allows the implementation of cluster validity technique in PNN using Ward's method to get clusters. This procedure is completely automatic with no parameter adjusting and instantaneous training, has high ability in producing a good cluster number estimates and provides a new point of view to use PNN as unsupervised classifier. The obtained results showed that this approach gives an accurate classification with about 3.44% of error through a comparison with the real land use and provides a better performance when comparing to usual unsupervised classification methods (fuzzy c-means (FCM) and K-means).

[1]  John Tsimikas,et al.  On training RBF neural networks using input-output fuzzy clustering and particle swarm optimization , 2013, Fuzzy Sets Syst..

[2]  Miin-Shen Yang,et al.  A cluster validity index for fuzzy clustering , 2005, Pattern Recognit. Lett..

[3]  Witold Pedrycz,et al.  Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering , 2006, Neurocomputing.

[4]  A. Kannan,et al.  Color Image Classification and Retrieval using Image mining Techniques 1 , 2010 .

[5]  Nicolas Picard,et al.  Clustering species using a model of population dynamics and aggregation theory , 2010 .

[6]  Vinay Kumar Dadhwal,et al.  Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS , 2001 .

[7]  William F. Christensen,et al.  A quantitative method for clustering size distributions of elements , 2005 .

[8]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[9]  José A. Sobrino,et al.  An integrated modelling and remote sensing approach for hydrological study in arid and semi‐arid regions: the SUDMED Programme , 2008 .

[10]  B. Everitt,et al.  A Monte Carlo Study of the Recovery of Cluster Structure in Binary Data by Hierarchical Clustering Techniques. , 1987, Multivariate behavioral research.

[11]  Xu Tongyu,et al.  Power quality disturbances classification Based on , 2009 .

[12]  Mingjie Tang,et al.  Detection of Hidden Bruise on Kiwi fruit Using Hyperspectral Imaging and Parallelepiped Classification , 2012 .

[13]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[14]  Jaime Gomez-Gil,et al.  Leaf classification in sunflower crops by computer vision and neural networks , 2011 .

[15]  Weina Wang,et al.  On fuzzy cluster validity indices , 2007, Fuzzy Sets Syst..

[16]  Yibin Ying,et al.  Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats. , 2007, Analytica chimica acta.

[17]  Sofie Spatharis,et al.  Assessing the efficiency of clustering algorithms and goodness-of-fit measures using phytoplankton field data , 2012, Ecol. Informatics.

[18]  Ying Liu,et al.  A self-trained semisupervised SVM approach to the remote sensing land cover classification , 2013, Comput. Geosci..

[19]  Sung-Kwun Oh,et al.  Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields , 2009, IEEE Transactions on Neural Networks.

[20]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[21]  Jirí Grim,et al.  Iterative principles of recognition in probabilistic neural networks. , 2008, Neural networks : the official journal of the International Neural Network Society.

[22]  M. Benoît,et al.  Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France , 2014 .

[23]  Sung-Kwun Oh,et al.  Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet , 2011, Eng. Appl. Artif. Intell..

[24]  Athanasios V. Vasilakos,et al.  Comparison of computational intelligence based classification techniques for remotely sensed optical image classification , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Rajesh N. Davé,et al.  Validating fuzzy partitions obtained through c-shells clustering , 1996, Pattern Recognit. Lett..

[26]  Jianyu Yang,et al.  Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method , 2013, Math. Comput. Model..

[27]  Thair Nu Phyu.  Survey of Classification Techniques in Data Mining , 2009 .

[28]  Seim Timung,et al.  Prediction of flow pattern of gas-liquid flow through circular microchannel using probabilistic neural network , 2013, Appl. Soft Comput..

[29]  Adriano L. I. Oliveira,et al.  Letters: Novelty detection with constructive probabilistic neural networks , 2008 .

[30]  D. Lloyd,et al.  A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery , 1990 .

[31]  Dimitris K. Tasoulis,et al.  Generalized locally recurrent probabilistic neural networks with application to text-independent speaker verification , 2007, Neurocomputing.

[32]  G. Dedieu,et al.  Monitoring of irrigated wheat in a semi‐arid climate using crop modelling and remote sensing data: Impact of satellite revisit time frequency , 2006 .

[33]  Fevzullah Temurtas,et al.  An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease , 2012, Comput. Electr. Eng..

[34]  Robert H. Laprade.  Split-and-merge segmentation of aerial photographs , 1988, Comput. Vis. Graph. Image Process..

[35]  A. Chehbouni,et al.  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 , 2006 .

[36]  Tamer Ölmez,et al.  Segmentation of remote-sensing images by incremental neural network , 2005, Pattern Recognit. Lett..

[37]  Yiu-Fai Wong,et al.  A new clustering algorithm applicable to multispectral and polarimetric SAR images , 1993, IEEE Trans. Geosci. Remote. Sens..

[38]  Xiangjian He,et al.  Classification Techniques in Pattern Recognition , 2005 .

[39]  Ignacio Rojas,et al.  A New Clustering Technique for Function Approximation , 2005 .

[40]  S. Archana,et al.  Survey of Classification Techniques in Data Mining , 2014 .

[41]  C. Conese,et al.  Fuzzy classification of spatially degraded Thematic Mapper data for the estimation of sub-pixel components , 1996 .

[42]  Jun Zeng,et al.  Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission. , 2007, Journal of environmental sciences.

[43]  E. Anderson.  The Species Problem in Iris , 1936 .

[44]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[45]  Figen Özen,et al.  A New License Plate Recognition System Based on Probabilistic Neural Networks , 2012 .

[46]  D. F. Specht,et al.  Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[47]  Witold Pedrycz,et al.  A granular-oriented development of functional radial basis function neural networks , 2008, Neurocomputing.

[48]  Ashish Ghosh,et al.  Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems , 2011, Appl. Soft Comput..

[49]  Saroj K. Meher,et al.  A novel fuzzy classifier based on product aggregation operator , 2008, Pattern Recognit..

[50]  Farid Melgani,et al.  An explicit fuzzy supervised classification method for multispectral remote sensing images , 2000, IEEE Trans. Geosci. Remote. Sens..

[51]  Chung-Liang Chang,et al.  Classification of PM10 distributions in Taiwan , 2006 .

[52]  Hojjat Adeli,et al.  A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.

[53]  Xuewen Zhang,et al.  Remote sensing image segmentation based on self-organizing map at multiple-scale , 2007, Geoinformatics.

[54]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[55]  Ashish Ghosh,et al.  Fuzzy clustering algorithms for unsupervised change detection in remote sensing images , 2011, Inf. Sci..

[56]  Jijuan Cao,et al.  Application of probabilistic neural network in bacterial identification by biochemical profiles. , 2013, Journal of microbiological methods.

[57]  Giles M. Foody.  Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches , 2001, J. Geogr. Syst..

[58]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[59]  Seok-Beom Roh,et al.  The design methodology of radial basis function neural networks based on fuzzy K-nearest neighbors approach , 2010, Fuzzy Sets Syst..

[60]  Benjamin F. Zaitchik,et al.  Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity , 2005 .

[61]  Zekeriya Uykan,et al.  Analysis of input-output clustering for determining centers of RBFN , 2000, IEEE Trans. Neural Networks Learn. Syst..

[62]  S. Pal,et al.  Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation , 2000 .

[63]  Samavedham Lakshminarayanan,et al.  Variable predictive model based classification algorithm for effective separation of protein structural classes , 2008, Comput. Biol. Chem..

[64]  Benoît Duchemin,et al.  Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region , 2007 .

[65]  V. Simonneaux,et al.  The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco , 2008 .

[66]  Martin Brown,et al.  Linear spectral mixture models and support vector machines for remote sensing , 2000, IEEE Trans. Geosci. Remote. Sens..

[67]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[68]  Mohan Trivedi,et al.  Segmentation of a Thematic Mapper Image Using the Fuzzy c-Means Clusterng Algorthm , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[69]  Frederick Kaefer,et al.  A neural network application to consumer classification to improve the timing of direct marketing activities , 2005, Comput. Oper. Res..

[70]  Yu-Liang Hsu,et al.  ECG arrhythmia classification using a probabilistic neural network with a feature reduction method , 2013, Neurocomputing.

[71]  Stephen J. Ventura,et al.  Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin , 1997 .

[72]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[73]  Adriano Lorena Inácio de Oliveira,et al.  Novelty detection with constructive probabilistic neural networks , 2008, Neurocomputing.

[74]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[75]  Christine F. Braban,et al.  The application of hierarchical cluster analysis and non-negative matrix factorization to European atmospheric monitoring site classification , 2014 .

[76]  Edit J. Kaminsky,et al.  Neural network classification of remote-sensing data , 1995 .

[77]  Witold Pedrycz,et al.  Conditional fuzzy clustering in the design of radial basis function neural networks , 1998, IEEE Trans. Neural Networks.

[78]  José A. Sobrino,et al.  Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[79]  Ronan Bureau,et al.  Clustering files of chemical structures using the Székely-Rizzo generalization of Ward's method. , 2009, Journal of molecular graphics & modelling.

[80]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..