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

Temitope M. Ogungboyega; Kingsley M. Udofia; Chidinma N. Kalu

Research Article

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

by Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu

Communications on Applied Electronics

Foundation of Computer Science (FCS), NY, USA

Volume 7 - Issue 33

Published: Mar 2020

Authors: Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu

10.5120/cae2020652856

PDF

Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu . Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System. Communications on Applied Electronics. 7, 33 (Mar 2020), 13-18. DOI=10.5120/cae2020652856

                        @article{ 10.5120/cae2020652856,
                        author  = { Temitope M. Ogungboyega,Kingsley M. Udofia,Chidinma N. Kalu },
                        title   = { Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System },
                        journal = { Communications on Applied Electronics },
                        year    = { 2020 },
                        volume  = { 7 },
                        number  = { 33 },
                        pages   = { 13-18 },
                        doi     = { 10.5120/cae2020652856 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }

                        %0 Journal Article
                        %D 2020
                        %A Temitope M. Ogungboyega
                        %A Kingsley M. Udofia
                        %A Chidinma N. Kalu
                        %T Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System%T 
                        %J Communications on Applied Electronics
                        %V 7
                        %N 33
                        %P 13-18
                        %R 10.5120/cae2020652856
                        %I Foundation of Computer Science (FCS), NY, USA

Abstract

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.

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Index Terms

Computer Science

Information Sciences

No index terms available.

Keywords

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