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Reseach Article

Hyperspectral Image Classification using Softcomputing Techniques: A Review

by A. Rajitha, P. Bhargavi, S. Jyothi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 11
Year of Publication: 2018
Authors: A. Rajitha, P. Bhargavi, S. Jyothi
10.5120/ijca2018917731

A. Rajitha, P. Bhargavi, S. Jyothi . Hyperspectral Image Classification using Softcomputing Techniques: A Review. International Journal of Computer Applications. 182, 11 ( Aug 2018), 18-25. DOI=10.5120/ijca2018917731

@article{ 10.5120/ijca2018917731,
author = { A. Rajitha, P. Bhargavi, S. Jyothi },
title = { Hyperspectral Image Classification using Softcomputing Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 182 },
number = { 11 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number11/29865-2018917731/ },
doi = { 10.5120/ijca2018917731 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:08.775133+05:30
%A A. Rajitha
%A P. Bhargavi
%A S. Jyothi
%T Hyperspectral Image Classification using Softcomputing Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 11
%P 18-25
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hyperspectral image classification plays a major role in remote image analysis. Hyperspectral images provide both spatial details of airborne imagery and spectral resolution for spectroscopic analysis and narrow band analysis techniques. Available satellite sensors like Hyperion, Hy-Map and AVIRIS are good sources of hyperspectral data. Applications of hyperspectral images are remote sensing, seed viability study, biotechnology, environmental monitoring, medical diagnose, food, pharmaceuticals and so on. Traditional techniques are difficult to deal with hyperspectral images directly, because hyperspectral images have continuous narrow spectral bands. To overcome this, hyperspectral image classification can be done using different softcomputing techniques. Softcomputing is an emerging field consisting of Fuzzy Logic, Neural Network and Genetic Algorithms. This paper reviews how hyperspectral image classification can be done using different softcomputing techniques.

References
  1. A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science, vol. 228, no. 4704, pp. 1147–1153, Jun. 1985.
  2. R. Ablin, C. Helen Sulochana,“A Survey of Hyperspectral Image Classification”, IJARCCE Vol.2, Issue 8, August 2013, 2319-5940.
  3. R.J. AspinallJ, W.J. Marcus, and A.W. Boardman. “Considerations in collecting, processing, and analyzing high spatial resolution hyperspectral data for environmental investigations”. Journal of Geographical Systems. 2002,4, 15-29.
  4. Richard J. Aspinall,W. Andrew Marcus, Joseph W. Boardman,”Considerations in collecting, processing, and analysing high spatial resolution hyperspectral data for environmental investigations”, Journal of Geographical Systems, Volume 4, Issue 1, pp 15–29, March 2002.
  5. A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science, vol. 228, no. 4704, pp. 1147–1153, Jun. 1985.
  6. D. Lu & Q. Weng. , “A Survey of Image Classification methods and techniques for improving classification Performance”, International Journal of Remote Sensing, Vol 28, Issue 5, pp 823-870, 2007. Swati Wakode, Rakesh Mallesh, Mandar Wagh , fManisha P Mali ,” Classification of Unstructured Data using Soft Computing: A Survey”, International Journal of Computer Science and Information Technologies, Vol. 6 , Issue 3, pp 2868-2870, 2015.
  7. B. Anil Gavade, S. Vijay Rajrobi “Productivity estimation and condition Assessment of horticulture crop from satellite based high resolution imaginary: A Review”, User Interaction meet 2013, National Remote Sensing Center, Hyderabad, 21st and 22nd February 2013.
  8. D.Landgrebe, “Hyperspectral image data analysis”, IEEE Signal Process Magazine, Vol. 19, Issue 1, pp. 17–28, 2002.
  9. B. Guo, S. Gunn, R. Damper, and J. Nelson, “Customizing kernel functions for svm-based hyperspectral image classification”, IEEE Transaction on Image Processing, Vol. 17, Issue 4, pp. 622–629, 2008.
  10. H. Jiao, Y. Zhong, L. Zhang, and P. Li, “Unsupervised remote sensing image classification using an artificial dna computing,” International Conference on Computing, Networking and Communications, 2011.
  11. O. Eches, N. Dobigeon, C. Mailhes, and J. Tourneret, “Bayesian estimation of linear mixtures using the normal compositional model. application to hyperspectral imagery,” IEEE Transaction on Image Processing, vol. 19, no. 6, pp. 1403–1413, 2010.
  12. T. Liu, L. Zhang, P. Li, and H. Lin, “Remotely sensed image retrieval based on region-level semantic mining,” EURASIP J. Image and Video Processing, vol. 4, 2012.
  13. T. Sheath, G. Nagalaxmi, and S. Jyothi, “A Study on Hyperspectral Remote Sensing Classification”, International Journal of Computer Applications (0975 – 8887) International Conference on Information and Communication Technologies (ICICT- 2014).
  14. G. F. Hughes, “On the mean accuracy of statistical pattern recognizers, “IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55–63, Jan. 1968.
  15. K. Kavitha and. S. Arivashagan, “A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification”, ©2010 International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 15.
  16. Sneha Murmua ,Sujata Biswas, “Application of Fuzzy logic and Neural Network in Crop Classification: A Review”, International Conference On Water Resources, Coastal And Ocean Engineering (Icwrcoe 2015).
  17. Friedl, M.A, Brodley C.E, and Strahler A.H ,(1999), Maximizing land cover classification accuracies produced by decision trees at continental to global scales, IEEE Transactions on Geoscience and Remote Sensing, 37,969-977.
  18. Foody G.M, Cox D.P., (1994), Sub pixel landcover composition estimation using a linear mixture model and fuzzy membership functions, Int. Remote sensing, 15, 619- 631
  19. Aplin,P. Atkinson ,M.P,Curran,J.P., (1999), Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the united kingdom, Remote sensing of environment, 66, 206- 216.
  20. . Hodgson. M.E , John R.Jensen, Jason A.Tullis, Kevin .D.Riordan and Clerk M.Archer, (2003), Synergistic use Lidar and color Arial photography for mapping urban parcel imperviousness, Photogrammetric Engineering and remote sensing, 69, 973 – 980
  21. . Mitra,S. Rajat.K.De and Pal,S.K (1997),Knowledge- Based Fuzzy MLP for Classification and Rule Generation, IEEE Transactions on neural networks,8,1338-1350 .
  22. . Gong.P and Howaeth P.J, (1992), Frequency-based contextual classification and gray level vector reduction for land use identification, Photogrammetric Engineering and Remote sensing,,58,423-437.
  23. Magnussen.S, Boudewyn. P and Wulder.M, (2004), Contextual Classification of Landsat TM images to forest inventory cover types. International Journal of Remote Sensing. 24 2421-2440 .
  24. Kotsiantis.S.B, (2007), Supervised Machine Learning: A Review of Classification Techniques, Informatica 3, 249-268.
  25. Melesse .M. A and Jordan,J.D, (2002), Photogrammetric Engineering and Remote Sensing, 68.
  26. Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing", Communication of the ACM, vol. 37, no. 3, pp. 77-84, 1994.
  27. “Principles of Soft Computing”, by ,Dr. S. N. Sivanandam, Dr. S. N. Deepa , 2nd Edition.
  28. Sumit Ghosh, Qutaiba Razouqi,H. Jerry Schumacher, and Aivars Celmins “A Survey of Recent Advances in Fuzzy Logic in Telecommunications Networks and New Challenges”IEEE transactions on fuzzy systems, vol. 6, no. 3, August 1998 (443-447) .
  29. Kailan Shay, Zakur Hussain , Applying Fuzzy :ogic to Risk Assessment and Decision Making, Nonember-2013.
  30. Miss Maya, V. Mawale, Dr. Vinay Charan “Implementation and Simulation of Fuzzy Logic Controllers for productivity and fertility of soils and performance evaluation of Triangular Membership functions”, COMPUSOFT, An International joining of advanced Computer Technology, 3(a), September-2014. http://en.wikipedia.org/wiki/Artificial_neural_network.
  31. Sneha Murma, Sujatha Bizwas, “Applications of Fuzzy Logic and Neural Networks in crop classification: Review”, International Conference On Water Resources, Coastal And Ocean Engineering (Icwrcoe 2015), Aquatic Procedia 4(2015), 1204-1210.
  32. Swati Wakode, Rakesh Mallesh 1, Mandar Wagh 1, Manisha P Mali , “Classification of Unstructured Data using Soft Computing: A Survey” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2868-2870
  33. Yanbo Huagng, YUbn Lan, Steven J. Thomson , Alex Fang, Wesley C, Ronald E Lacey, “Development of Soft Computing and applications in agricultural and biological engineering, Computers and Electronics in Agriculture, 7/2010 107-127.
  34. F. Wang , Geosci. Remote Sens. IEEE Trans. 28 (2) (1990) 194–201 .
  35. Z. Wang , X. Sun , D. Zhang , in: Advanced intelligent computing theories and applications. With aspects of artificial intelligence, 2007,pp.377-384.
  36. G.S. Dwarakish, B. Nithyapriya ”Application of soft computing techniques in coastal study –A review,” Journal of Ocean Engineering and Science 1 (2016) 247–255.
  37. Naveen J. P. Anne et. At. “Modeling Soil parameters using hyperspectral image reflectance in subtopical coastal wetlands” , International journal of Applied Earth observation and Geoinfomatics” , 2014, pp 47-56.
  38. Papageorgiou, Elpiniki I., Athanasis T, Markinos, and T. A. Gemtos. “Fuzzy cognitive map based approach for predicting yield in cotton crop production on a basis for decision support system in precision agriculture application” Applied Soft Computing 11.4(2011): 3643-3657.
  39. Zhu A Hudson, B. Burt, J. Lubich, “soil mapping using GIS ,expert knowledge and fuzzy logic “, soil science , Sociecty of American Journal, 65, 1463-1472.
  40. Shi X, Zhu A, Burt J, F Simsoon, ”A case based necessary approach to fuzzy system mapping”, 68, 885-894.
  41. W.McCulloch , W. Pitts , Bull. Math. Biophys. 5 (1943) 115–133 .
  42. F. Rosenblatt , Psychol. Rev. 65 (1958) 386–408 .
  43. H. Elarabi, K. Ali ,” Soil classification modeling using Artificial Neural Network”, International Conference on Intelligent System , Dec 2008.
  44. Zhengyong Zhao, Thien Lien Chow, Herb W. Rees, Qi Yang, Zisheng Xing, Fan-Rui Meng, “Predict soil texture distributions using an artificial neural network model”, Computers and Electronics in Agriculture 65(2009) 36-48.
  45. Li R., Mukaidono, M., Burhan Turksen, I., 2002. A fuzzy neural network for pattern classification and feature selection. Fuzzy Sets and Systems 130, 101 – 108.
  46. Qiu, F., 2008. Neuro-fuzzy Based Analysis of Hyperspectral Imagery. Photogrammetric Engineering & Remote Sensing 74(10), 1235–1247.
  47. Verbeiren, S., Eerens, H., Piccard, I., Bauwens, I., V. Orshoven, J., 2008. Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium. International Journal of Applied Earth Observation and Geoinformation, International Journal of Applied Earth Observation and Geoinformation 10(4), 486–497.
  48. Wang, Y., Jamshidi, M.,2007. A Hierarchical Fuzzy Classification Scheme for Remote Sensing Data. Intelligent Automation and Soft Computing 13(4), 463-476.
  49. Wei, W., Guanglai, G., 2008. An application of neuro-fuzzy system in remote sensing image classification. International Conference on Computer Science and Software Engineering, IEEE, 1069-1072.
  50. Soo-See Chai, Bert Veenendaal, Geoff West, Jeffrey Philip Walker, “Backpropagation neural networks for soil moisture retrieval using NAFE’05 data:
  51. Odhiambo, L.O., Yoder, R.E., Yoder, D.C., 2001a. Estimation of reference crop evapo transpiration using fuzzy state models. Transactions of the ASAE 44(3), 543–550.
  52. Freeland, R.S., Odhiambo, L.O., 2007. Subsurface characterization using textural features extracted from GPR data. Transactions of the ASABE 50 (1), 287–293.
  53. Bajwa, S.G., Bajcsy, P., Groves, P., Tian, L.F., 2004. Hyperspectral image data mining for band selection in agricultural applications. Transactions of the ASAE 47 (3), 895–907.
  54. Fidencio, P.H., Ruisanchez, I., Poppi, R.J., 2001. Application of artificial neural networks to the classification of soils from Sao Paulo state using near-infrared spectroscopy. Analyst 126, 2194–2200.
  55. Altendorf, C.T., Elliott, R.L., Stevens, E.W., Stone, M.L., 1999. Development and validation of a neural network model for soil water content prediction with comparison to regression techniques. Transactions of the ASAE 42 (3), 691–699.
  56. Zhang, Z.X., Kushwaha, R.L., 1999. Application of neural networks to simulate soil–tool interaction and soil behavior. Canadian Agricultural Engineering 41 (2), 119–125.
  57. Yang, C.C., Prasher, S.O., Lacroid, R., Kim, S.H., 2004b. Application of multivariate adaptive regression splines (MARS) to simulate soil temperature. Transactions of the ASAE 47 (3), 881–887.
  58. Pachepsky, Y., Acock, B., 1998. Stochastic imaging of soil parameters to assess variability and uncertainty of crop yield estimates. Geoderma 85 (2–3), 213–229.
  59. Parasuraman, K., Elshorbagy, A., Si, B.C., 2007. Estimating saturated hydraulic conductivity using genetic programming. Soil Science Society of America Journal 71, 1676–1684.
  60. Papageorgiou, Elpiniki I., Athanasios T. Markinos, and T. A. Gemtos. "Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application." Applied Soft Computing 11.4 (2011): 3643- 3657.
  61. Chen, Yun, Shahbaz Khan, and Zahra Paydar. "To retire or expand? A fuzzy GIS-based spatial multi-criteria evaluation framework for irrigated agriculture." Irrigation and drainage 59.2 (2010): 174-188.
Index Terms

Computer Science
Information Sciences

Keywords

Hyperspectral Image classification Fuzzy Logic Artificial Neural Networks Genetic Algorithm