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Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System

Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu

Temitope M Ogungboyega, Kingsley M Udofia and Chidinma N Kalu. Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System. Communications on Applied Electronics 7(33):13-18, March 2020. BibTeX

	author = {Temitope M. Ogungboyega and Kingsley M. Udofia and Chidinma N. Kalu},
	title = {Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System},
	journal = {Communications on Applied Electronics},
	issue_date = {March 2020},
	volume = {7},
	number = {33},
	month = {Mar},
	year = {2020},
	issn = {2394-4714},
	pages = {13-18},
	numpages = {6},
	url = {},
	doi = {10.5120/cae2020652856},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper aimed at developing a model that will aid the process of identifying and extracting earth surface features from satellite images using adaptive neuro-fuzzy inference system. Conventional methods of classifying earth features (Normalized Difference Vegetation Index, NDVI, and Normalized Difference Water Index, NDWI) were first used to generate the data for the training of the ANFIS model using the three bands in Landsat 8 (band 2: Blue, band 4: Red and band 5: NIR). The performance of the developed ANFIS model was validated using four different satellite images and the results compared with the conventional classification methods. An accuracy level of 98.66 – 99.88 % with a RMSE of 0.0218 – 0.0506 were obtained.


  1. Tri Dev Acharya, Dong Ha Lee, In Tae Yang and Jae Kang Lee “ Identification of water bodies in a Landsat 8 OLI Image Using a J48 Decision Tree” 2016.
  2. Aksoy, E.; Ozsoy, G.; Dirim, M.S. (2009). Soil mapping approach in GIS using LandSat Satellite imagery and DEM data. African Journal of Agriculture Research vol.4 (11), pp. 1295-1302.
  3. Warren, A., S. Batterbury and H. Osbahr, 2001. Soil erosion in the West African Sahel: A review and an application of a local political ecology approach in South West Niger. Global Environ. Change, 11: 79-95.
  4. Bationo, A., A. Hartemink, O. Lungu, M. Naimi, P. Okoth, E. Smaling and L. Thiombiano, 2006. African Soils: Their productivity and profitability of fertilizer use. Background Paper, Africa Fertilizer Summit, June 9-13, 2006, Abuja, pp: 25.
  5. FAO. 1995. Land and environmental degradation and desertification in Africa. The FAO Corporative Documents Repository. Food and Agriculture Organization, Rome, Italy.
  6. Kalra, N. K., Singh, L., Kachhwah, R. and Joshi, D. C. (2010). Remote sensing and GIS in identification of soil constraints for sustainable development in Bhilwara district, Rajasthan. Journal of the Indian Society of Remote Sensing 38(2): 279-290.
  7. Venkataratnam, L. (1980), Use of remotely sensed data for soil mapping. Photonirvachak 8, 19-26.
  8. Kudrat, M., S.K. Saha & A.K.Tiwari, (1990), Potential use of IRS LISS II digital data in soil landuse mapping and productivity assessment, Asian Pacific Remote Sensing Journal, 2, pp 73-78.
  9. Karale, R.L., (1992), Remote sensing with IRS-1A in soil studies, development, status and prospects. pp 128- 143. In, R.L. Karale (ed.), Natural Resources Management- A New Perspective. NNRMS, Bangalore.
  10. Asadi, S. S., Vasantha Rao, B. V. T. and Sekar, S. (2012). Creation of physical characteristics information for Natural Resources Management Using remote sensing and GIS: A Model study. International Journal of Modern Engineering Research 2(2): 226-232.
  11. Gates, D.M. 1970. Physicl and Physiological properties of plants. In: Remote sensing with special reference to Agriculture and Forestry. National Academy of Sciences, Washington, D. C. pp. 224-252.
  12. Gausam, H.W., Escobar, D.E, and E.B. Knipling. 1977. Relation of Peperomia obtusifolia’s anomalous leaf reflectance to its leaf anatomy. Photogrammetric Engineering and Remote Sensing. 43:1183-1185.
  13. Williams, D.L. 1991. A comparison of spectral reflectance properties at the needle, branch and canopy level for selected conifer species. Remote Sensing of Environment. 35: 79-93.
  14. Ripple, W.J. 1986. Spectral reflectance relationships to leaf water stress. Photogrammetric Engineering and Remote Sensing. 52: 1669-1675.
  15. Hunt, E.R. Jr. and B.N. Rock. 1989. Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote sensing of Environment. 1: 155-159.
  16. Cohen, W.B. 1991. Response of vegetation indices to changes in three measures of leaf water stress. Photogrammetric Engineering and remote sensing. 57: 195-202.
  17. Chuvieco, E. 1998. El factor temporal en teledetección: evolución fenomenológica y análisis de cambios. Revista de Teledetección, 10: 1–9.
  18. DeFries, R. and J. Townshend, NDVI-Derived Land Cover Classifications at a Global Scale, Int.J.Remote Sens. 15 (1994), pp. 3567-3586.
  19. Garrigues, D. S. Allard and F. Baret, Using First-and Second-Order Variograms for Characterizing Landscape Spatial Structures from Remote Sensing Imagery, Geoscience and Remote Sensing, IEEE Transactions on. 45 (2007), pp. 1823-1834.
  20. Jackson RD, Huete AR (1991) Interpreting vegetation indexes. Prev Vet Med 11:185–200
  21. Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995). The interpretation of spectral vegetation indexes. IEEE T Geosci Remote 33:481–486
  22. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432.
  23. Jang R., “Neuro-Fuzzy Modeling,” proceedings of the IEEE, vol. 83, no. 3, 1995.
  24. Hamdan H., Garibaldi M. (2010). Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival. WCCI 2010, IEEE World Congress on Computational Intelligence, pp. 18–23.
  25. Rezaei K., Hosseini R., Mazinani M., “A Fuzzy Inference System for Assessment of the Severity of the peptic ulcers,” Computer Science & Information Technology, DOI: 10.5121/ .2014.4527, pp. 263-271, 2014.
  26. Mohd Salleh, M. N., Talpur, N. and Hussain, K. (2017). Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions. Tan et al. (Eds.): DMBD, LNCS 10387, pp. 527–535, 2017.DOI: 10.1007/978-3-319-61845-6 52.


Spectral signature, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Adaptive Neuro-Fuzzy Inference System (ANFIS), Near-Infrared (NIR).