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

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

Crynodeb

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.

Iaith wreiddiol	Saesneg
Teitl	2018 Open Innovations Conference, OI 2018
Golygyddion	Cecil Ouma
Cyhoeddwr	Institute of Electrical and Electronics Engineers Inc.
Tudalennau	165-169
Nifer y tudalennau	5
ISBN (Electronig)	9781538653166
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/OI.2018.8535671
Statws	Cyhoeddwyd - 15 Tach 2018
Cyhoeddwyd yn allanol	Ie
Digwyddiad	2018 Open Innovations Conference, OI 2018 - Johannesburg, De Affrica Hyd: 3 Hyd 2018 → 5 Hyd 2018

Cyfres gyhoeddiadau

Enw	2018 Open Innovations Conference, OI 2018

Cynhadledd

Cynhadledd	2018 Open Innovations Conference, OI 2018
Gwlad/Tiriogaeth	De Affrica
Dinas	Johannesburg
Cyfnod	3/10/18 → 5/10/18

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

NDC 7 Ynni Fforddiadwy a Glân

Mynediad at Ddogfen

10.1109/OI.2018.8535671

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

Chikohora, E., Esiefarienrhe, B. M., & Chikohora, T. T. (2018). Analysis and Performance Evaluation of Parameterization Algorithms in Remote Sensing Image Processing. Yn C. Ouma (Gol.), 2018 Open Innovations Conference, OI 2018 (tt. 165-169). erthygl 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. yn C Ouma (gol.), 2018 Open Innovations Conference, OI 2018., 8535671, 2018 Open Innovations Conference, OI 2018, Institute of Electrical and Electronics Engineers Inc., tt. 165-169, 2018 Open Innovations Conference, OI 2018, Johannesburg, De Affrica, 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. gol. / Cecil Ouma. Institute of Electrical and Electronics Engineers Inc., 2018. t. 165-169 8535671 (2018 Open Innovations Conference, OI 2018).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

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

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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.

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Y2 - 3 October 2018 through 5 October 2018

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