Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing

Edmore Chikohora; Bokuhwo M. Esiefarienrhe; Teressa T. Chikohora

doi:10.1109/OI.2018.8535671

Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing

Edmore Chikohora, Bokuhwo M. Esiefarienrhe, Teressa T. Chikohora

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The study reviews currently used Feature Extraction Techniques (FET) and analyze their parameterization strategies as discussed by different authors, thereby setting the ground to do a performance evaluation of the GenApp, a novel adaptive algorithm for parameterization of FET that was introduced in our previous publication. We performed efficiency analysis, worst-case analysis and fitness value tests to the feature extraction algorithms to evaluate their strengths in a comparative manner. The results obtained from the experiments reflect a marginally higher complexity value on the execution of the GenApp, a reduced number of generations in finding an optimum parameter value and a relatively constant fitness value which gives us confidence in the algorithm's potential to improve parameterization and output images from FET.

Original language	English
Title of host publication	2018 Open Innovations Conference, OI 2018
Editors	Cecil Ouma
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	165-169
Number of pages	5
ISBN (Electronic)	9781538653166
DOIs	https://doi.org/10.1109/OI.2018.8535671
Publication status	Published - 15 Nov 2018
Externally published	Yes
Event	2018 Open Innovations Conference, OI 2018 - Johannesburg, South Africa Duration: 3 Oct 2018 → 5 Oct 2018

Publication series

Name	2018 Open Innovations Conference, OI 2018

Conference

Conference	2018 Open Innovations Conference, OI 2018
Country/Territory	South Africa
City	Johannesburg
Period	3/10/18 → 5/10/18

Keywords

Big Oh notation
Dimensionality reduction
Feature extraction
Fitness value
Growth rate function
Parameterization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/OI.2018.8535671

Cite this

Chikohora, E., Esiefarienrhe, B. M., & Chikohora, T. T. (2018). Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing. In C. Ouma (Ed.), 2018 Open Innovations Conference, OI 2018 (pp. 165-169). Article 8535671 (2018 Open Innovations Conference, OI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/OI.2018.8535671

@inproceedings{872b22974c844e9fb1f3c8b9102a4c5e,

title = "Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing",

abstract = "The study reviews currently used Feature Extraction Techniques (FET) and analyze their parameterization strategies as discussed by different authors, thereby setting the ground to do a performance evaluation of the GenApp, a novel adaptive algorithm for parameterization of FET that was introduced in our previous publication. We performed efficiency analysis, worst-case analysis and fitness value tests to the feature extraction algorithms to evaluate their strengths in a comparative manner. The results obtained from the experiments reflect a marginally higher complexity value on the execution of the GenApp, a reduced number of generations in finding an optimum parameter value and a relatively constant fitness value which gives us confidence in the algorithm's potential to improve parameterization and output images from FET.",

keywords = "Big Oh notation, Dimensionality reduction, Feature extraction, Fitness value, Growth rate function, Parameterization",

author = "Edmore Chikohora and Esiefarienrhe, {Bokuhwo M.} and Chikohora, {Teressa T.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Open Innovations Conference, OI 2018 ; Conference date: 03-10-2018 Through 05-10-2018",

year = "2018",

month = nov,

day = "15",

doi = "10.1109/OI.2018.8535671",

language = "English",

series = "2018 Open Innovations Conference, OI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "165--169",

editor = "Cecil Ouma",

booktitle = "2018 Open Innovations Conference, OI 2018",

}

Chikohora, E, Esiefarienrhe, BM & Chikohora, TT 2018, Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing. in C Ouma (ed.), 2018 Open Innovations Conference, OI 2018., 8535671, 2018 Open Innovations Conference, OI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 165-169, 2018 Open Innovations Conference, OI 2018, Johannesburg, South Africa, 3/10/18. https://doi.org/10.1109/OI.2018.8535671

Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing. / Chikohora, Edmore; Esiefarienrhe, Bokuhwo M.; Chikohora, Teressa T.
2018 Open Innovations Conference, OI 2018. ed. / Cecil Ouma. Institute of Electrical and Electronics Engineers Inc., 2018. p. 165-169 8535671 (2018 Open Innovations Conference, OI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing

AU - Chikohora, Edmore

AU - Esiefarienrhe, Bokuhwo M.

AU - Chikohora, Teressa T.

PY - 2018/11/15

Y1 - 2018/11/15

N2 - The study reviews currently used Feature Extraction Techniques (FET) and analyze their parameterization strategies as discussed by different authors, thereby setting the ground to do a performance evaluation of the GenApp, a novel adaptive algorithm for parameterization of FET that was introduced in our previous publication. We performed efficiency analysis, worst-case analysis and fitness value tests to the feature extraction algorithms to evaluate their strengths in a comparative manner. The results obtained from the experiments reflect a marginally higher complexity value on the execution of the GenApp, a reduced number of generations in finding an optimum parameter value and a relatively constant fitness value which gives us confidence in the algorithm's potential to improve parameterization and output images from FET.

AB - The study reviews currently used Feature Extraction Techniques (FET) and analyze their parameterization strategies as discussed by different authors, thereby setting the ground to do a performance evaluation of the GenApp, a novel adaptive algorithm for parameterization of FET that was introduced in our previous publication. We performed efficiency analysis, worst-case analysis and fitness value tests to the feature extraction algorithms to evaluate their strengths in a comparative manner. The results obtained from the experiments reflect a marginally higher complexity value on the execution of the GenApp, a reduced number of generations in finding an optimum parameter value and a relatively constant fitness value which gives us confidence in the algorithm's potential to improve parameterization and output images from FET.

KW - Big Oh notation

KW - Dimensionality reduction

KW - Feature extraction

KW - Fitness value

KW - Growth rate function

KW - Parameterization

UR - http://www.scopus.com/inward/record.url?scp=85059029090&partnerID=8YFLogxK

U2 - 10.1109/OI.2018.8535671

DO - 10.1109/OI.2018.8535671

M3 - Conference contribution

AN - SCOPUS:85059029090

T3 - 2018 Open Innovations Conference, OI 2018

SP - 165

EP - 169

BT - 2018 Open Innovations Conference, OI 2018

A2 - Ouma, Cecil

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 Open Innovations Conference, OI 2018

Y2 - 3 October 2018 through 5 October 2018

ER -

Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Cite this