A Clonal Selection Algorithm for Classification of Mangroves Remote Sensing Image

Luo, Yanmin; Liao, Minghong

doi:10.14257/ijca.2014.7.4.36

Cited by 4 publications

(1 citation statement)

References 13 publications

(8 reference statements)

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“…2 shows the setting of ROIs. Maximum likelihood method was used for image classification which is high frequency [4], [5]. Table 2 shows the ROI setting.…”

Section: Data Acquisitionmentioning

confidence: 99%

Environmental Change of Saemangeum Tideland based on Remote Sensing Method

Lee¹,

Yun

2015

Advanced Science and Technology Letters

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Reclamation project is very efficient development plan which saves the sea to create new land in case of Republic of Korea where the population density is high and urbanization is still in progress. However, reclamation project is necessary to be managed consistently because to install the artificial structures in natural terrain may cause the environmental change. Thus, in this study, we suggested the remote sensing method as the efficient management plan about large-scale reclamation project and derived quantitative environmental change based on this method.

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“…2 shows the setting of ROIs. Maximum likelihood method was used for image classification which is high frequency [4], [5]. Table 2 shows the ROI setting.…”

Section: Data Acquisitionmentioning

confidence: 99%

Environmental Change of Saemangeum Tideland based on Remote Sensing Method

Lee¹,

Yun

2015

Advanced Science and Technology Letters

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Knowledge extraction of agricultural data using artificial immune system

Lasisi

Ghazali

Herawan

et al. 2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

Lasisi

Ghazali

Deris

et al. 2016

Int. J. Patt. Recogn. Artif. Intell.

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Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

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A Clonal Selection Algorithm for Classification of Mangroves Remote Sensing Image

Abstract: Abstract

Cited by 4 publications

References 13 publications

Environmental Change of Saemangeum Tideland based on Remote Sensing Method

Environmental Change of Saemangeum Tideland based on Remote Sensing Method

Knowledge extraction of agricultural data using artificial immune system

Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

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