A Design Entropy Based Hybrid Soft Classifier Algorithms for Improving Classification Performance of a Satellite Data

Year 2019, , 117 - 124, 30.08.2019

Ranjana Sharma P. K. Garg R. K. Dwivedi

https://doi.org/10.33187/jmsm.459653

Abstract

Image classification of the satellite imagery interprets the thematic map to represent the spatial distribution of earth features. There are so many applications of Remote sensing image classification such as Resource utilization and environmental impact analysis etc. The overall process result depends on two main aspects Every object have distinctive signature and feature of interest The process can distinguish these features separately. Image classification is broadly classified in two ways Pure classification and Mixed classification. In the pure classification, the pixels are classified into class only and in the mixed classification, pixels can fit into one or more module according to their membership values. In hard classification, data may be lost because of the restriction being in a single class. However in the soft classification, this problem is resolved. After resolving the problem, there is a need of accuracy assessment. Accuracy parameter is very important factor in terms of classification. So, in this study, I am trying to design the algorithm for the hybridization classification with entropy to maintain the optimizing.

Keywords

Accuracy, Entropy, Fuzzy C-Means with entropy

References

• [1] M. K. Arora, A. Peterson, (Land cover classification from remote sensing data, GIS development6, 2002,24-25, 30-31.
• [2] M. A.Aziz, Evaluation of soft classifiers for remote sensing data, Abstr. Appl. Anal.,unpublished Ph.D thesis, Indian Institute of Technology Roorkee,Roorkee,India. (2004).
• [3] J.C.Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithm,Plenum, New York, USA,(1981), ISBN: 978-1-4757-0452-5.
• [4] J.C.Bezdek,R.Ehrlich,W.Full, The fuzzy c-means clustering algorithm, Computers and Geosciences,,1984,10,191-203.
• [5] R. G.Congalton, A review of assessing the accuracy of classifications of remotely sensed data, (1991),37,35-47.
• [6] H.Dehghan,H.Ghassemian, Measurement of uncertainty by the entropy: application to the classification of MSS data, International journal of remote sensing,(2006) vol.27, no. 18, 4005–4014.
• [7] J.C.Dunn, Well Separated Clusters and Optimal Fuzzy Partitions, Journal of Cybernetics,(1981),4,95-104.
• [8] R.k.Dwivedi,S. K.Ghosh, P.Roy, Optimization of Fuzzy Based Soft Classifiers for Remote Sensing Data,ISPRS-International Archives of the Photogrammetric, Remote Sensing and Spatial Information Sciences 1,(2012),385-390.
• [9] R.k.Dwivedi,S. K.Ghosh, and Anil Kumar, Investigation Of Image Classification Techniques For Performance Enhancement,International Journal of Management and Technology, Volume 3, number 1, (2012),21-33.
• [10] R.k.Dwivedi,S. K.Ghosh, Visualization of Uncertainty usingentropy on Noise clustering with entropy classifier, 3rd IEEE International Advance Computing Conference, (IACC-2013).(2013), ISBN:-978-1-4673-4528-6.
• [11] J.R.Eastman, R.M.Laney, Bayesian soft classification for sub-pixel analysis: critical evaluation, Photogrammetric Engineering and Remote Sensing,68, (2002),1149-1154.
• [12] D.Ferna.Ndez-Prieto, An iterative approach to partially supervised classification problems, International Journal of Remote Sensing, 23,(2002), 3887–3892.
• [13] G.M.Foody,Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data, Journal of Remote Sensing, vol.17, no. 7,(1996),1317–1340.
• [14] G.M.Foody,Estimation of sub-pixel land cover composition in the presence of untrained classes,Computers and Geosciences, vol. 26, no. 4, (2000),469–478.
• [15] S. R.Kannan,R. Devi,S.Ramathilagam,K.Takezawa,Effective FCM noise clustering algorithms in medical images,Computers in Biology and Medicine,(2013), vol. 43, no.2, 73–83.
• [16] J.Kivinen,M.Warmuth, Boosting as Entropy Projection,Proc. 12th Ann.Conf. Computational Learning Theory, (1999),134–144.
• [17] R.Krishnapuram, J.M.Keller, A possiblistic approach to clustering,IEEE Transactions on Fuzzy Systems, 1,(1993), 98-108.
• [18] A.Kumar, V. K.Dadhwal,Entropy based fuzzy classification parameter optimization using uncertainty variation across spatial resolution,Journal of Indian Society of Remote Sensing, (2010),Vol 38, No. 2,179-192.
• [19] R.P.Li, M.Mukaidono, Gaussian clustering method based on maximum-fuzzyentropy interpretation,Fuzzy Sets and Systems 102,(1999) 253–258.
• [20] D.Lu,Q. Weng, A survey of image classification methods and techniques for improving classification performance,International Journal of Remote Sensing, 28(5),(2007),823–870.
• [21] A.M.Massone,F.Masulli,A.Petrosini, Fuzzy clustering algorithms and Landsat images for detection of waste areas: A comparison,In Advances in Fuzzy Systems and Intelligent Technologies,Proc. WILF ’99, Italian Workshop on Fuzzy Logic,Shaker Publishing, Maastricht, The Netherlands,(2000), 165-175.
• [22] S.Miyamoto,M.Mukaidono, Fuzzy c-means as a regularization and maximumentropy approach,In: Proc. of the 7th International Fuzzy Systems Association World Congress, Prague, Czech,(1997), June 25-30, 1997, vol. II, 86–92.
• [23] K.Oki,T.M.Uenishi,K.Omasa, M.Tamura, Accuracy of land cover area estimation from coarse spatial resolution images using an unmixing method,International Journal of Remote Sensing, 25, (2004),1673-1683.
• [24] I.Olthof, D.J.King, R.A.Lautenschlager, Mapping deciduous forest ice storm damage using Landsat and environmental data,Remote Sensing of Environment, 89, (2004),484–496.
• [25] R.L.Powell,N.Matzke,C.De Souza JR,M.Clark,I.Numata,L.L.Hess, D.A.Roberts, Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon,Remote Sensing of Environment, 90, (2004),221–234.
• [26] Priyadarshi Upadhyay, S. K. Ghosh, and Anil Kumar, A Brief Review of Fuzzy Soft Classification and Assessment of Accuracy Methods for Identification of Single Land Cover,Studies in Surveying and Mapping Science (SSMS) American Society of Science and Engineering, Volume 2, (2014),ISSN 2328-6245.
• [27] R.k.Dwivedi,S. K.Ghosh,A.Kumar, Investigation Of Image Classification Techniques For Performance Enhancement,Viewpoint “An International Journal of Management and Technology” ,(2012),ISSN-2229-3825.
• [28] Ranjana Sharma,Achal Kumar Goyal,Achal Kumar Goyal, A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment, Proceedings of Fifth International Conference on Soft Computing for Problem Solving , Springer Singapore,(2016),vol.437,629-639.
• [29] S. S.Sengar,A.Kumar,H. R.Wason,S. K.Ghosh,Krishna Murthy,sh, Study of soft classification approaches for identification of earthquake-induced liquefied soil,Geomatics, Natural Hazards and Risk(ahead-of-print), (2013),1-19.
• [30] M.A.Shalan,M.K.Arora,S.K.Ghosh, An evaluation of fuzzy classifications from IRS 1C LISS III imagery: a case study, International Journal of Remote Sensing,24,(2003), 3179-3186.
• [31] C. E.Shannon, A Mathematical Theory of Communication,Bell System Technical Journal 27(3), (1948),379–423.
• [32] C. E.Shannon, Prediction and Entropy of Printed English,Bell System Technical Journal 30 (1), (1951),50–64.
• [33] Xiao-Hong Wu,Jian-Jiang Zhou, Alternative Noise Clustering Algorithm,IEEE- ICSP Proceedings,(2006),0-7803-9737-1/06.
• [34] A.N.Tihonov,V.Y.Arsenin, Solutions of Ill-Posed Problems,. Wiley, New York,(1977).
• [35] V.Vapnik, The Nature of Statistical Learning Theory,Springer Verlag, New York,(1995).
• [36] V.Vapnik, IStatistical Learning Theory, John Wiley and Sons,New York,(1998).
• [37] V. N.Vapnik, An overview of statistical learning theory,IEEE Transactions of Neural Networks, 10, (1999),988-999.
• [38] V. N.Vapnik , The Nature of Statistical Learning Theory,2nd Edition,(2000).
• [39] J.Zhang,G.M.Foody, A fuzzy classification of sub-urban land cover from remotely sensed imagery,International Journal of Remote Sensing, 19,(2001), 2721-2738.