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An Improve Shannon Fano Data Compression Algorithm using Residue Number System

T.D. Lawal L.O. Olatunbosun K.A. Gbolagade
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Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System

by Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 33
Year of Publication: 2020
Authors: Temitope M. Ogungboyega, Kingsley M. Udofia, Chidinma N. Kalu
10.5120/cae2020652856

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 },
issue_date = { Mar 2020 },
volume = { 7 },
number = { 33 },
month = { Mar },
year = { 2020 },
issn = { 2394-4714 },
pages = { 13-18 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number33/867-2019652856/ },
doi = { 10.5120/cae2020652856 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T20:02:43.348206+05:30
%A Temitope M. Ogungboyega
%A Kingsley M. Udofia
%A Chidinma N. Kalu
%T Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 33
%P 13-18
%D 2020
%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

Keywords

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

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