Land cover classification from multispectral data using computational intelligence tools: A comparative study

Information (Switzerland)

Volumn 8, Issue 4, 2017, Pages

  Mora, André ^a   Santos, Tiago M A ^a   Lukasik, Szymon ^b,c   Silva, João M N ^d   Falcão, António J ^a   Fonseca, José M ^a   Ribeiro, Rita A ^a  

^a UNINOVA   (Portugal)

^b AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

^d INSTITUTO SUPERIOR DE AGRONOMIA   (Portugal)

Author keywords

Aggregation operators; Image fusion; Land cover classification; Remote sensing

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; IMAGE FUSION; INTELLIGENT CONTROL; MATHEMATICAL OPERATORS; PERSONNEL TRAINING; REMOTE SENSING; SATELLITE IMAGERY; SPECTROSCOPY;

AGGREGATION OPERATOR; COMPARATIVE STUDIES; COMPUTATIONAL INTELLIGENCE METHODS; COMPUTATIONAL INTELLIGENCE TECHNIQUES; FUZZY INFERENCE METHOD; LAND COVER CLASSIFICATION; SATELLITE IMAGE CLASSIFICATION; UNSUPERVISED APPROACHES;

IMAGE CLASSIFICATION;

EID: 85036460977     PISSN: None     EISSN: 20782489     Source Type: Journal    
DOI: 10.3390/info8040147     Document Type: Article

References (41)

1. Santos, T.M.A.; Mora, A.; Ribeiro, R.A.; Silva, J.M.N. Fuzzy-fusion approach for land cover classification. In Proceedings of the 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES), Budapest, Hungary, 30 June-2 July 2016; pp. 177-182
2. Temudo, M.P.; Silva, J.M.N. Agriculture and forest cover changes in post-war Mozambique. J. Land Use Sci. 2012, 7, 425-442
3. Friedl, M.A.; Brodley, C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399-409
4. Schowengerdt, R.A. Remote Sensing: Models and Methods for Image Processing; Academic Press: Cambridge, MA, USA, 2006
5. Ishibuchi, H.; Nozaki, K.; Yamamoto, N.; Tanaka, H. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets Syst. 1994, 65, 237-253
6. Nguyen, C.H.; Pedrycz, W.; Duong, T.L.; Tran, T.S. A genetic design of linguistic terms for fuzzy rule based classifiers. Int. J. Approx. Reason. 2013, 54, 1-21
7. Shackelford, A.K.; Davis, C.H. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2354-2363
8. Barros, R.C.; Basgalupp, M.P.; de Carvalho, A.C.P.L.F.; Freitas, A.A. A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 2012, 42, 291-312
9. Belgiu, M.; Draguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 2016, 114, 24-31
10. Millán, V.E.G.; Sanchez-Azofeifa, G.A.; Malvárez, G.C. Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 3081-3094
11. Ahmed, B.; Al Noman, M.A. Land cover classification for satellite images based on normalization technique and Artificial Neural Network. In Proceedings of the 2015 1st International Conference on Computer and Information Engineering (ICCIE), Rajshahi, Bangladesh, 26-27 November 2015; pp. 138-141
12. Castelluccio, M.; Poggi, G.; Sansone, C.; Verdoliva, L. Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. arXiv, 2015
13. Sharapov, R.; Varlamov, A. Using neural networks in remote sensing monitoring of exogenous processes. In Proceedings of the Sixth International Conference on Graphic and Image Processing (ICGIP 2014), Beijing, China, 24-26 October 2014
14. Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85-117
15. Ayhan, E.; Kansu, O. Analysis of image classification methods for remote sensing. Exp. Tech. 2012, 36, 18-25
16. Mather, P.; Tso, B. ClassificationMethods for Remotely Sensed Data, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2009
17. Fauvel, M.; Chanussot, J.; Benediktsson, J.A. Decision Fusion for the Classification of Urban Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2828-2838
18. Ribeiro, R.A.; Falcão, A.; Mora, A.; Fonseca, J.M. FIF: A fuzzy information fusion algorithm based on multi-criteria decision making. Knowl.-Based Syst. 2014, 58, 23-32
19. Rudas, I.J.; Pap, E.; Fodor, J. Information aggregation in intelligent systems: An application oriented approach. Knowl.-Based Syst. 2013, 38, 3-13
20. Beliakov, G.; Pradera, A.; Calvo, T. Aggregation Functions: A Guide for Practitioners; Springer Publishing Company: Heidelberg, Germany, 2007; Volume 221
21. Ribeiro, R.A.; Pais, T.C.; Simões, L.F. Benefits of Full-Reinforcement Operators for Spacecraft Target Landing. In Preferences and Decisions, Models and Applications; Greco, S., Marques Pereira, R.A., Squillante, M., Yager, R.R., Kacprzyk, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 353-367
22. Torra, V.; Narukawa, Y. Modeling Decisions: Information Fusion and Aggregation Operators; Springer: Berlin/Heidelberg, Germany, 2007; Volume XIV
23. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man. Cybern. 1979, 9, 62-66
24. Marquardt, D.W. An algorithm for least-squares estimation of non-linear parameters. J. Soc. Ind. Appl. Math. 1963, 11, 431-441
25. Sugeno, M. An introductory survey of fuzzy control. Inf. Sci. 1985, 36, 59-83
26. Yager, R.R.; Rybalov, A. Full reinforcement operators in aggregation techniques. IEEE Trans. Syst. Man Cybern. Part B 1998, 28, 757-769
27. Calvo, T.; Mayor, G.; Mesiar, R. Aggregation Operators: New Trends and Applications; Physica: Berlin/Heidelberg, Germany, 2012
28. Rekik, A.; Zribi, M.; Benjelloun, M.; Ben Hamida, A. A k-means clustering algorithm initialization for unsupervised statistical satellite image segmentation. In Proceedings of the 2006 1ST IEEE International Conference on E-Learning in Industrial Electronics, Hammamet, Tunisia, 18-20 December 2006; pp. 11-16
29. Mitra, S.; Kundu, P.P. Satellite image segmentation with Shadowed C-Means. Inf. Sci. 2011, 181, 3601-3613
30. Pal, S.K.; Mitra, P. Multispectral image segmentation using the rough-set-initialized EM algorithm. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2495-2501
31. Bandyopadhyay, S.; Maulik, U. Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognit. 2002, 35, 1197-1208
32. Omkar, S.N.; Kumar, M.M.; Mudigere, D.; Muley, D. Urban Satellite Image Classification using Biologically Inspired Techniques. In Proceedings of the IEEE International Symposium on Industrial Electronics, Vigo, Spain, 4-7 June 2007; pp. 1767-1772
33. Kanellopoulos, I.; Wilkinson, G.G. Strategies and best practice for neural network image classification. Int. J. Remote Sens. 1997, 18, 711-725
34. Ross, T.J. Fuzzy Logic with Engineering Applications; John Wiley & Sons: Chichester, UK, 2009
35. Mendel, J.M. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions; Prentice Hall PTR: Upper Saddle River, NJ, USA, 2001
36. Mamdani, E.H.H.; Assilian, S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 1975, 7, 1-13
37. Nedeljkovic, I. Image classification based on fuzzy logic. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004, 34, 685
38. Ruthenberg, H.; MacArthur, J.D. Farming Systems in the Tropics; Clarendon Press: Oxford, UK, 1980
39. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Technical Report for Texas A&M University; Texas A&M University: College Station, TX, USA, 1974
40. Garcia, M.L.; Caselles, V. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto Int. 1991, 6, 31-37
41. Rand, M. Objective Criteria for the Evaluation of Methods Clustering. J. Am. Stat. Assoc. 1971, 66, 846-850